
==== Front
Expert Rev Pharmacoecon Outcomes Res
Expert Rev Pharmacoecon Outcomes Res
Expert Review of Pharmacoeconomics & Outcomes Research
1473-7167
1744-8379
Taylor & Francis

38860294
10.1080/14737167.2024.2366439
2366439
Version of Record
Research Article
Original Research
Cost-effectiveness of a multicomponent-adherence intervention in fracture liaison services
L. MAAS ET AL.
EXPERT REVIEW OF PHARMACOECONOMICS & OUTCOMES RESEARCH
https://orcid.org/0000-0002-0032-0712
Maas Lieke a
https://orcid.org/0000-0003-0682-9533
Boonen Annelies a b
https://orcid.org/0000-0003-0528-8931
Li Nannan a
https://orcid.org/0000-0001-7662-3990
Wyers Caroline E. c d
https://orcid.org/0000-0003-3984-2232
Van den Bergh Joop P. b c d
https://orcid.org/0000-0003-4274-9258
Hiligsmann Mickaël a
a Department of Health Services research, Care and Public Health Research Institute (CAPHRI), Maastricht University , Maastricht, The Netherlands
b Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Center , Maastricht, The Netherlands
c Department of Internal Medicine, VieCuri Medical Center , Venlo, The Netherlands
d Department of Internal Medicine, NUTRIM, Maastricht University Medical Center , Maastricht, The Netherlands
CONTACT Lieke Maas Lieke.maas@maastrichtuniversity.nl Department of Health Services research, Care and Public Health Research Institute (CAPHRI), Maastricht University, P.O Box 616, MD, Maastricht 6200, The Netherlands
11 6 2024
2024
11 6 2024
24 8 987996
Integra05 9 2024
Integra05 9 2024
08 4 2024
28 5 2024
© 2024 Maastricht University. Published by Informa UK Limited, trading as Taylor & Francis Group.
2024
Maastricht University
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Background

This study aims to assess the lifetime cost-effectiveness of a multi-component adherence intervention (MCAI), including a patient decision aid and motivational interviewing, compared to usual care in patients with a recent fracture attending fracture liaison services (FLS) and eligible for anti-osteoporosis medication (AOM).

Research design and methods

Data on AOM initiation and one-year persistence were collected from a quasi-experimental study conducted between 2019 and 2023 in two Dutch FLS centers. An individual level, state-transition Markov model was used to simulate lifetime costs and quality-adjusted life years (QALYs) with a societal perspective of MCAI vs usual care. One-way and probabilistic sensitivity analyses were conducted including variation in additional FLS and MCAI costs (no MCAI cost in baseline).

Results

MCAI was associated with gain in QALYs (0.0012) and reduction in costs (-€16) and is therefore dominant. At the Dutch willingness-to-pay threshold of €50,000/QALY, MCAI remained cost-effective when increasing costs of the FLS visit or the yearly maintenance cost for MCAI up to +€60. Probabilistic sensitivity analysis demonstrated MCAI to be dominant in 54% of the simulations and cost-effective in 87% with a threshold of €50,000/QALY.

Conclusions

A MCAI implemented in FLS centers may lead to cost-effective allocation of resources in FLS care, depending on extra costs.

KEYWORDS

Adherence
cost-effectiveness
fracture
fracture liaison services
osteoporosis
shared decision-making
ZonMw research line Rational Pharmacotherapy 848016001 This study was funded by the ZonMw research line Rational Pharmacotherapy, grant number 848016001. METC study number is METC2028-0507-A-10. The study was registered in the Netherlands Trial Registry, part of the Dutch Cochrane Centre (Trial NL7236 (NTR7435)). Version 1.0; 26-11-2020.
==== Body
pmc1. Introduction

Since the initiation of fracture liaison services (FLS) in 2007, this care innovation has gained global recognition as an effective approach to prevent subsequent fractures related to osteoporosis or bone fragility. As of April 2024, the International Osteoporosis Foundation reported that over 55 countries have implemented more than 900 FLS centers in their post-fracture care [1]. By aiming to screen all patients with recent fractures, the FLS services strive to identify and treat persons at risk for subsequent fractures, more specifically those with osteoporosis or prevalent vertebral fractures. To date, 17 FLS centers have been identified in the Netherlands including those in Maastricht University Medical Center (MUMC+) and VieCuri Venlo [1].

A large body of literature has shown FLS to be effective in reducing fractures and also cost-effective [2]. In 2018, a systematic literature review by Wu et al. identified 23 published studies and conference abstracts that revealed the cost-effectiveness, or even the dominance of FLS [3]. Recent studies have confirmed the cost-effectiveness of FLS in countries such as Finland and the Netherlands [4,5]. The first cost-effectiveness study conducted in the Netherlands used a real-world population and FLS data and modeled both costs and quality-adjusted life years (QALYs) from a societal perspective with a lifetime horizon [5]. This study demonstrated that FLS was cost-effective with €45 higher costs and 0.11 additional QALY gained, leading to an incremental cost-effectiveness ratio (ICER) of €409 per QALY gained.

One of the key cost-effectiveness drivers of treatment of osteoporosis is adherence to anti-osteoporosis medication (AOM) [6]. Despite increase of treatment initiation and adherence within FLS, adherence is still suboptimal ranging between 55% and 75%, suggesting the need for interventions to further improve adherence in the context of FLS [4,7–9]. Recent literature highlights that these suboptimal adherence levels in osteoporosis are mainly due to the lack of understanding regarding consequences of non-initiation of and non-adherence to AOM [10]. Shared decision-making (SDM) tools offer incorporation of patient preferences and values as well as an in-depth conversation between the health professional and the patient regarding benefits, harms, and burdens of treatment options available [11–13]. A systematic literature review on interventions to improve adherence in patients with osteoporosis suggested that multi-component adherence interventions (MCAI) based on a combination of patient education and counseling lead to better outcomes. Particularly, the use of patient decision aids (PDAs) when AOM is indicated [12,13] as well as motivational interviews during follow-up care [12,14–20] demonstrated most potential in enhancing AOM adherence.

Also, when it concerns adherence interventions, lifetime cost-effectiveness analyses are important to facilitate decisions on implementations of such care innovations in daily practice. Previous studies have assessed the potential clinical and economic impact of improving adherence using hypothetical behavioral interventions. For example, the study of Hiligsmann et al. demonstrated that poor adherence to AOM (adherence <80%) in osteoporosis patients without a fracture resulted in a decrease of about 50% in potential benefits observed in clinical trials [21]. Another study with the same patient population suggested that a PDA improving treatment initiation rates or persistence by 20% was cost-effective [22]. All these previous studies were however based on simulations in non-FLS settings and cost-effectiveness analyses that incorporated real-life adherence interventions are currently lacking.

The Improvement of osteoporosis Care Organized by Nurses (ICON) study was developed as a quasi-experimental pragmatic study to test the effect and cost-effectiveness of a MCAI combining a PDA [23] with motivational interviews compared to usual care (UC) [24]. The ICON study was conducted among patients attending the FLS after a recent fracture and eligible for AOM treatment [24,25]. This study aims to estimate the lifetime incremental cost-effectiveness of a MCAI compared to UC in these patients from a societal perspective.

2. Methods

A previously developed lifetime Markov microsimulation model [5] which estimated the societal cost-effectiveness of having an FLS compared to no FLS for persons older than 50 years and a recent fracture from a Dutch societal perspective was adapted for the purpose of this study. Only patients actually attending the FLS and having an indication for AOM were considered. The model was built using TreeAge Pro 2024 software. The guideline of IOF-ESCEO [26], as well as the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement [27] was used to design and report our study (Supplementary Tables S1 and S2). Patients willing to participate provided written informed consent for study participation including permission to obtain data from their pharmacy to assess AOM adherence. The Medical Ethics Committee of MUMC+/Maastricht University (UM), the Netherlands, approved this study. This study was completed in accordance to the Declaration of Helsinki.

2.1. MCAI and setting

The protocol and results of the MCAI study that was the basis for the data on AOM initiation and persistence required for the model have been published [24]. In short, the ICON study was designed as a 1-year quasi-experimental (pre-post) pragmatic study testing superiority of the MCAI compared to UC on AOM persistence in persons attending the FLS and having an indication to start OAM. The ICON study was conducted in two Dutch FLS centers between 2019 and 2023. Potential contamination of the UC group by the nurse-administered MCAI was prevented by initiating inclusion of the MCAI group only after UC group had completed the FLS follow-up visit and after osteoporosis nurses were trained in the use of the PDA and motivational interviewing. FLS care in both centers consisted of two visits with a care professional, ranging between 30 and 40 minutes per visit. No difference in provided usual care was observed between the two FLS centers.

The MCAI comprised shared decision-making using a PDA on starting AOM in addition to lifestyle during the first FLS visit followed by motivational interviewing on the use of AOM at follow-up visit between 3 and 4 months after the initial visit. An 8-page PDA that was specifically developed for use in this population was available to guide healthcare professionals and patients to make a shared decision on starting AOM and the preferred type of AOM [28]. In line with the International Patient Decision Aids Standards (IPDAS) development procedure [29], the PDA included information on fractures and osteoporosis, initiation of AOM treatment, general risk factors for subsequent fractures, the role of lifestyle, personalized risk of a subsequent fracture with and without AOM treatment, and AOM treatment options and their characteristics. Three versions of the PDA were created based on the level of future fracture risk and its accompanying most effective treatment options. Analyses on clinical effectiveness of MCAI showed a small improvement although not significant on AOM persistence at one year (patients with oral bisphosphonates MCAI: 84% vs UC: 80%). However the process of shared decision-making after 4 months was significantly in favor of MCAI [25].

2.2. Model structure and treatment pathways

The lifetime model of Li et al. that assessed the cost-effectiveness of FLS vs non-FLS in patients with and without presence of osteoporosis was adjusted to solely include FLS attenders with osteoporosis or prevalent vertebral fractures (and this requiring AOM) and was used to estimate the cost-effectiveness of introducing the MCAI compared to usual care. The population’s gender and age distribution, type of initial fracture (‘index fracture’), rate of AOM initiation, and persistence was substituted with the ICON data. The model structure includes a decision tree followed by a state-transition Markov model (see Figure 1). The two branches of the decision tree reflected whether patients received the MCAI or UC. Each branch was stratified by gender to adjust for risk of a subsequent fracture between genders. Patients enter the Markov model based on their index fracture and moved in the Markov model according to probability of having (or not) a subsequent fracture (hip, vertebral or non-hip and non-vertebral) or dying. Based on probabilities, patients could stay in the subsequent fracture state or could change to a different fracture state or die. Patients in both branches differed only in the chance to initiate and remain persistent to AOM. Figure 1. Markov model. Patients enter the model with a recent fracture (index Fx). From there, patients can either have a subsequent fracture, remain in the same state, or die. At any state, patients can move to death. NHNV, non-hip and non-vertebral; Fx, fracture; CV, clinically vertebral.

Being an ‘’individual” state transition model, the Markov model used tracker variables to record the total number of subsequent fractures in terms of fracture type. The lifetime horizon included 6-month cycles as recommended by the IOF-ESCEO guideline [26]. Discount rate of 1.5% for QALY and 3% for costs were used, based on the new Dutch guideline of economic evaluations in healthcare [30]. Table 1 illustrates key model input parameters.Table 1. Key model input parameters.

Parameter	Data	Data source	
Women	Men	
Gender	79.0%	21.0%	ICON, 2019–2023	
A distribution of starting age	0.5% (50–55 years), 9.8% (56–60 years), 19.2% (61–65 years), 19.7% (66–70 years), 16.6% (71–75 years), 14.5% (76–80 years),12.4% (81–85 years), 5.2% (86–90 years), 2.1% (90+ years)	ICON, 2019–2023	
Mortality	 	 	
All-cause mortality (per 1000) for the general population	2.2 (50–54 years), 3.7 (55–59 years), 6.1 (60–64 years), 9.6 (65–69 years), 15.3 (70–74 years), 26.7 (75–79 years), 50.3 (80–84 years), 103.1 (85–89 years), 198.8 (90–94 years), 352.3 (95+years)	3.1 (50–54 years), 5.0 (55–59 years), 8.4 (60–64 years), 13.8 (65–69 years), 22.9 (70–74 years), 39.8 (75–79 years), 72.9 (80–84 years), 138.2 (85–89 years), 243.6 (90–94 years), 401.7 (95+years)	[31]	
Excess mortality after subsequent fracture	2.90 (2.52–3.34)	3.76 (3.20–4.42)	[32,33]	
Fracture risk (annual rate per 1000 person-years) of the general Dutch population	
Hip	0.266 (50–54 years), 0.587 (55–59 years), 1.066 (60–64 years), 1.504 (65–69 years), 2.450 (70–74 years), 4.183 (75–79 years), 7.814 (80–84 years), 16.727 (85–89 years), 21.645 (90+years)	0.352 (50–54 years), 0.320 (55–59 years), 0.623 (60–64 years), 0.900 (65–69 years), 1.257 (70–74 years), 3.082 (75–79 years), 4.450 (80–84 years), 8.833 (85–89 years), 17.704 (90+years)	[5]	
CV	0.433 (50–54 years), 0.652 (55–59 years), 1.213 (60–64 years), 1.820 (65–69 years), 2.404 (70–74 years), 3.578 (75–79 years), 5.113 (80–84 years), 4.568 (85–89 years), 4.456 (90+years)	0.480 (50–54 years), 0.735 (55–59 years), 0.762 (60–64 years), 0.825 (65–69 years), 1.437 (70–74 years), 1.706 (75–79 years), 2.383 (80–84 years), 3.296 (85–89 years), 2.379 (90+years)	 	
NHNV	12.476 (50–54 years), 16.053 (55–59 years), 17.424 (60–64 years), 20.809 (65–69 years), 22.365 (70–74 years), 24.896 (75–79 years), 27.800 (80–84 years), 32.475 (85–89 years), 36.172 (90+years)	10.864 (50–54 years), 10.405 (55–59 years), 10.198 (60–64 years), 8.811 (65–69 years), 9.348 (70–74 years), 9.218 (75–79 years), 11.036 (80–84 years), 12.915 (85–89 years), 15.362 (90+years)	 	
Relative risk of having fracture for osteoporosis vs non-osteoporosis patients	 	
Hip	5.66 (50–59 years), 3.39 (60–69 years), 2.25 (70–79 years), 1.57 (80+years)	[34]	
CV	2.68 (50–59 years), 2.18 (60–69 years), 1.77 (70–79 years), 1.51 (80+years)	 	
NHNV	2.25 (50–59 years), 1.90 (60–69 years), 1.61 (70–79 years), 1.42 (80+years)	 	
Relative risk of having a subsequent fracture	Pooled RR: 1.84 (1.72–1.97)	Pooled RR: 1.92 (1.56–2.34)	[35]	
First-year cost of a fracture (estimated in €2023)	 	
Hip	22,389	19,491	[36]	
Hip, yearly long-term cost	4,702	Dutch standard daily nursing home cost*365*21%	
CV	14,453	11,837	[36]	
NHNV	7,858	7,377	[36]	
First-year productivity cost of a fracture (estimated in €2023)	
Hip	9,416 (50–54 years), 9,179 (55–59 years), 8,694 (60–64 years)	[37,38]	
CV	7,514 (50–54 years), 7,324 (55–59 years), 6,938 (60–64 years)	 	
NHNV	6,087 (50–54 years), 5,934 (55–59 years), 5,621 (60–64 years)	 	
Health state utility values	 	 	 	
Baseline (patients after a recent fracture)	0.813 (50–59 years), 0.813 (60–69 years), 0.809 (70–79 years), 0.665 (80+years)	0.855 (50–59 years), 0.821 (60–69 years), 0.841 (70–79 years), 0.743 (80+years)	[39]	
Hip (1st year/subsequent years)	0.55 (0.53–0.57)/0.86 (0.84–0.89)	[40]	
CV (1st year/subsequent years)	0.68 (0.65–0.70)/0.85 (0.82–0.87)	 	
NHNV (1st year/subsequent years)	0.79 (0.65–0.93)/0.95 (0.81–1.09)	[41]	
Fracture risk reduction (expressed as relative risk compared to no treatment) of medications for patients on medication	
Hip	0.67 (0.48–0.96)	[42,43]	
CV	0.45 (0.31–0.65)	
NHNV	0.81 (0.46–1.44)	
AOM initiation	 	 	
MCAI	96.3%	ICON, 2019–2023	
UC	98.3%	 	
AOM adherence (persistence and initiation)	 	
MCAI	84.0% (0.5 year), 75.3% (0.75 year), 71.6% (1 year)	ICON, 2019–2023	
UC	80.3% (0.5 year), 75.2% (0.75 year), 70.0% (1 year)	 	
Treatment costs per patient (estimated in €2023)	 	
Drug cost for one year	Oral bisphosphonates: 139	[44]	
Cost per side effect	1.84 (1st cycle), 0.94 (subsequent cycles)	[45]	
One-time FLS-related costs per patient	
FLS cost (€2023)	515	[46]	
Improvement of osteoporosis Care Organized by Nurses, ICON; Clinically vertebral, CV; Non-hip and non-vertebral, NHNV; Anti-Osteoporosis Medication, AOM; Multi-component adherence intervention, MCAI; Usual care, UC; Fracture liaison service, FLS.

2.3. Model input data

2.3.1. Patient sample, osteoporosis and fracture risk

The starting age distribution was 0.5% (50–55 years), 9.8% (56–60 years), 19.2% (61–65 years), 19.7% (66–70 years), 16.6% (71–75 years), 14.5% (76–80 years),12.4% (81–85 years), 5.2% (86–90 years), 2.1% (90+ years), of which 79% women. To estimate the fracture risk of the population of interest for the current model, the annual fracture incidence (hip, vertebral, or non-hip and non-vertebral (NHNV)) from the general Dutch population of 50 years and older [5,42] was used as a starting point, but consecutively adjusted to account for the increase on fracture rate in those having osteoporosis and having had a recent fracture. As can be seen in Table 1, the increased fracture risk due to osteoporosis was estimated based on data from Hiligsmann & Reginster and estimated for, namely at 5.66 (50–59 years), 3.39 (60–69 years), 2.25 (70–79 years), and 1.57 (80+ years) [34]. The relative risk of subsequent fractures in those with osteoporosis and an initial fracture after an initial was estimated to be Dutch studies [35]. Pooled relative risk for women was 1.84 (1.72–1.97) and 1.92 (1.56–2.34) for Dutch men.

2.3.2. Mortality

Baseline mortality per age group for male and female were based on the Dutch official registry Central Bureau of Statistics (CBS) [31]. Lifetime mortality risk after a recent fracture with a relative risk was 2.90 for females and 3.76 for males, based on a meta-analysis [32]. This excess mortality was adjusted to be attributable to the possible fractures, assumed at 25% of excess mortality, instead of also other factors such as comorbidities [33,47,48].

2.3.3. Utility values

Health-related utility per age and gender classification was based on Dutch patients visiting the FLS of VieCuri Medical Center [39]. Baseline utility for females ranged from 0.813 (50–59 years) to 0.665 (80+ years). Utility for males ranges from 0.855 (50–59 years) to 0.743 (80+ years) [39]. The effects of fractures on utility in subsequent years were derived from the International Costs and Utilities Related to Osteoporotic Fractures Study (ICUROS) [40]. The disutility multipliers for NHNV fractures were not included in ICUROS and hence found elsewhere [41].

2.3.4. Drug treatment effects and costs

The pooled treatment efficacy relative risk was distinguished between hip (RR: 0.67, 95% CI: 0.48–0.96), vertebral (RR: 0.45, 95% CI: 0.31–0.65), and NHNV fractures (RR: 0.81, 95% CI: 0.46–1.44), based on the network meta-analysis of the National Institute for Clinical Health and Excellence (NICE) for patients being persistent [43]. Costs of one year treatment with oral bisphosphonates was estimated at €139 (in the year 2023), based on Dutch data [44]. The model assumed AOM are prescribed for one year. Costs of side effects were calculated according to Li et al. [5], assuming an additional 0.041 consultations with a general practitioner during the first cycle (6 months) and 0.021 consultations during the following cycle [45]. Based on the national guideline, costs of a visit at the general practitioner in 2023 was estimated at €44.96 and were assumed once per year.

2.3.5. Fracture cost

Both first-year healthcare and non-healthcare costs (estimated in €2023) per fracture type were included in this study, with a societal perspective. First-year fracture costs per gender was based on a Dutch study [36]. Additionally, long-term annual nursing costs were calculated for hip fractures which was estimated at €29,049 (daily nursing home cost * 365) with an average of 21% of patients being hospitalized due to hip fractures [36]. First-year productivity costs for paid work were based on the friction cost method, in line with the Dutch guideline for economic evaluation in healthcare [49] as well as on work-related absence rates per fracture type (hip: 0.99, vertebral: 0.79, non-hip and non-vertebral: 0.64) [37]. No productivity costs for unpaid work were included for patients over the age of 65.

2.3.6. Treatment initiation and persistence with MCAI and UC

AOM persistence was calculated for 6 months, 9 months, and 1 year after the first FLS visit and based on the one-year data collected via pharmacy dispensing during the ICON study. For the MCAI group, persistence was 84.0%, 75.3%, 71.6% at 6 months, 9 months, and 1 year after FLS. In the UC group, persistence was 80.3%, 75.2%, 70.0% at 6 months, 9 months, and 1 year after. AOM initiation, based on data from the ICON study [25], was 96.3% and 98.3% for MCAI and UC, respectively. A drug treatment duration of maximum one year was simulated, after which the effect of drug treatment on fractures regressed to zero over a period similar to treatment duration. Also, no additional effect of the MCAI on persistence and this fracture risk was assumed beyond the first year.

2.3.7. FLS-related costs

Diagnosis treatment combination (DBC) system was used to determine the average one-time cost of FLS care in the Netherlands (€515 in 2023) for both treatment arms, which includes costs for diagnostics such as dual energy X-ray absorptiometry (DXA) scan and lab tests, visitations with osteoporosis nurse/specialized nurse and medical specialists at the FLS, as well as one extra general practitioner consultation per year during follow-up [46]. No additional costs for the MCAI was considered.

2.4. Analyses and outcomes

2.4.1. Base case analysis

By using a 1st order Monte-Carlo simulation, 1,000,000 trials were run. Point estimates of model input parameters were used to estimate total costs (healthcare and productivity costs), number of total prevented fractures, and total QALYs for MCAI and UC, as well as the incremental costs, incremental QALYs, and incremental cost-effectiveness ratio (ICER). The ICER of MCAI vs UC was expressed in €2023 per QALY gained. Multiple scenario analyses were performed to estimate the cost-effectiveness of MCAI in patients at different starting ages (50–90 years).

In the Netherlands the willingness-to-pay threshold ranges between €20,000 and €80,000 according to disease severity following the proportional shortfall approach [50,51]. The willingness-to-pay threshold in our population would be €20,000/QALY. However, as the MCAI concerns a preventative intervention, the more recently proposed willingness-to-pay threshold of €50,000 per QALY gained was used [52].

2.4.2. One-way sensitivity analysis

1,000,000 trials were also run, each time with adjusting one singular variable. Along with running the model with a healthcare instead of a societal perspective and a shorter time horizon (10 years instead of lifetime), other variables were also consecutively changed. The other variable changes include: (i) discount rate (3% for both costs and QALY); (ii) gender (100% male or female); (iii) baseline utility (−20%) and baseline utility based on ICON data (female: 50–59 years 0.807, 60–69 years 0.750, 70–79 years 0.811, 80–89 years 0.841, 90+ years, 0.852); (iv) relative risk of subsequent fracture associated with osteoporosis (−25%); (v) additional costs for annual maintenance of the MCAI tool including literature updates (+10, +20, +30, +40, +50, +60, +70 €); (vi) fracture costs (±25%); (vii) drug costs (±50%); (viii) nursing home costs (±25%); and (ix) excess mortality attribution probability (0% and −25%); (x) equal initiation rates in UC and MCAI based on MCAI initiation rates; (xi) additional costs of the FLS- (+69€) to account for potential extra time as a consequence of shared decision-making (12.5% of our patients in the MCAI intervention arm requested deliberation time after the first visit and an extra telephone consultation). Additional analyses assumed 50% and 100% of patients requiring a paid phone consultation.

2.4.3. Probabilistic sensitivity analysis

Using a 2nd order Monte-Carlo simulation, the model was run 200 times based on 25,000 trials per pathway. Results were presented using the cost-effectiveness plane as well as a cost-effectiveness acceptability curve (CEAC), which demonstrates the probability of MCAI being cost-effective compared to UC as function of the willingness-to-pay threshold. To account for uncertainties in input point estimates and joint uncertainty surrounding multiple variables, distributions for several variables were used. Normal distributions were used for fracture cost, excess mortality attribution probability, nursing home cost, probability of nursing home admission, and productivity cost using a standard deviation of 20% of the mean due to the lack of standard error. Log-normal distributions were used for relative risk of having a subsequent fracture, treatment efficacy, and excess mortality after a fracture. Lastly, beta distributions were used for fracture incidence based on the number of fractures and age range of 70–74 years, and the effect of fracture on utility based on a 95% confidence interval.

3. Results

The base-case analysis demonstrated that MCAI was dominant with a lower societal cost of €16, somewhat more QALYs gained (0.0012), and 0.0005 fractures prevented in MCAI compared to UC in patients visiting the FLS with a recent fracture and eligible for AOM (Table 2). MCAI was dominant for different ages (Table 3). The largest decrease in costs of MCAI compared to UC was shown in patients aged 90 years. The highest QALY gain of MCAI compared to UC was seen in patients aged 70 years.Table 2. Per patient average lifetime costs, accumulated QALYs, number of fractures, incremental costs and QALY, and ICER of base-case analysis.

 	MCAI	UC	Incremental	
Costs	€19,865	€19,881	€-16	
QALYs	9.4944	9.4932	0.0012	
Fractures prevented	1.5116	1.5167	−0.005	
ICER	-	-	Dominant	
Multi-component adherence intervention, MCAI; Usual care, UC; Quality-adjusted life years, QALY; Incremental cost effectivity ratio, ICER.

Table 3. One-way sensitivity analyses on ICER MCAI vs UC.

 	 	Incremental cost	Incremental QALY	ICER	
Base-case	-	€ −16	0.0012	Dominant	
Age	50 years	€ −13	0.0010	Dominant	
60 years	€ −17	0.0013	Dominant	
70 years	€ −23	0.0014	Dominant	
80 years	€ −23	0.0008	Dominant	
90 years	€ −35	0.0009	Dominant	
Perspective	Healthcare	€ −20	0.0012	Dominant	
Gender	Male	€ −11	0.0007	Dominant	
Female	€ −23	0.0013	Dominant	
RR of subsequent Fx in case when a previous fx is present	−25%	€ −12	0.0011	Dominant	
RR of subsequent Fx in case of osteoporosis	−25%	€ −14	0.0011	Dominant	
Excess mortality	0%	€ −24	0.0009	Dominant	
−50%	€ −23	0.0014	Dominant	
Baseline utility	−20%	€ −14	0.0012	Dominant	
ICON data	€ −23	0.0014	Dominant	
Drug cost	+50%	€ −18	0.0012	Dominant	
−50%	€ −27	0.0012	Dominant	
Fracture cost	+25%	€ −27	0.0014	Dominant	
−25%	€ −16	0.0015	Dominant	
Nursing home cost	+25%	€ −16	0.0010	Dominant	
−25%	€ −16	0.0008	Dominant	
Additional costs of MCAI	+€10	€ −10	0.0010	Dominant	
+€20	€ −5	0.0013	Dominant	
+€30	€ 7	0.0012	5,833	
+€40	€ 17	0.0011	15,455	
+€50	€ 29	0.0011	26,364	
+€60	€ 47	0.0010	47,000	
+€70	€ 61	0.0008	76,250	
Additional costs FLS for phone consultation	12.5%	€ −11	0.0010	Dominant	
50%	€ 14	0.0011	12,727	
100%	€ 49	0.0013	37,692	
AOM initiation	Equal	€ −25	0.0013	Dominant	
Discount rate	3% QALY	€ −19	0.0010	Dominant	
Time horizon	10 years	€ −21	0.0012	Dominant	
Quality-adjusted life years, QALY; Incremental cost effectivity ratio, ICER; RR, relative risk; sub., subsequent; fracture, Fx; multi-component adherence intervention, MCAI.

All one-way sensitivity analysis remained similar to the base-case analysis, demonstrating dominant ICERs (Table 3). MCAI leads to more QALY gained and higher costs decrease in females compared the males. When including additional yearly costs MCAI with a cumulative increase of €10, MCAI remained dominant below €20 and cost-effective at the threshold of €50,000/QALY gained below €60 (see Figure 2). When accounting for additional remote consultation deliberation time requested by 12.5% of our patients, and thus requiring an additional phone consultation, MCAI remained dominant. MCAI remained cost-effective for 100% of patients requesting additional paid deliberation time. Figure 2. Costs in € per QALY gained for additional costs of the MCAI by €10 with a willingness-to-pay threshold of €50,000 per QALY gained. Quality-adjusted life years, QALY; Willingness to pay, WTP; multi-component adherence intervention, MCAI.

With a willingness-to-pay threshold of €50,000/QALY gained, the CEAC of the probabilistic sensitivity analysis demonstrated that MCAI was cost-effective in 87% of the simulations compared to UC (Figure 3). At threshold €80,000/QALY, 89% of the simulations demonstrated MCAI to be cost-effective compared to UC. The CEAC demonstrated 54% of dominant simulations. Figure 3. (a) cost-effectiveness plane of MCAI with a willingness-to-pay threshold of €50,000/QALY gained. (b) acceptability curve of MCAI compared to UC. Willingness to pay, WTP; multi-component adherence intervention, MCAI; quality-adjusted life years, QALY; usual care, UC.

4. Discussion

This study is to our knowledge the first to assess the lifetime cost-effectiveness of a MCAI in persons attending the FLS and having an indication to start AOM. With demonstrated cost-effectiveness of FLS in the Netherlands [5], limited marginal gain on the potential economic value of adding a MCAI in FLS was expected. Although the effect of MCAI on persistence in the ICON study was small and non-significant [25], results demonstrated MCAI to be dominant (lower costs, more QALY). Due to the already high AOM persistence in the UC group, fracture trackers showed little increase in prevented fractures, namely five subsequent fractures prevented over lifetime for every 1000 patients receiving MCAI. This small number of fractures prevented could also partially be explained by the fact that the treatment duration was limited to one-year maximum, in line with the trial duration. This furthermore underestimates the long-term benefits of the MCAI, as persistence at one-year was slightly higher compared to UC.

The sensitivity analyses revealed that the additional costs of the MCAI is the key driver of cost-effectiveness. Nonetheless, MCAI remained dominant below an additional price of €20 and cost-effective below €60 with a threshold of €50,000/QALY. The results are overall in line with previous simulations models. For example, Hiligsmann et al. found a hypothetical adherence tool costing approximately €150 with an improved adherence of 10% to result an ICER of €32,906 [21]. Penton et al. assumed the same adherence tool to be less effective and less expensive with a similar ICER of $44,837, approximately €30,922 [22]. The cost-effectiveness is also dependent on patients requiring an additional phone consultation during FLS care. Notwithstanding, MCAI remained dominant based on data from our sample (12.5%) and remained cost-effective if 100% of the total FLS attenders requiring AOM with a recent fracture requested additional paid deliberation time.

Notably, whilst AOM initiation and persistence in our sample (from two experienced FLS centers) were relatively high and included AOM initiation of 96.3% and 98.3% and AOM 6-month persistence of 84% and 80% for MCAI and UC respectively, data from other FLS centers in the country and around the world could offer different, perhaps more impactful, results in adherence improvement. Sensitivity analysis based on equal AOM initiation rates in both arms showed relatively higher QALY gain. Nonetheless, as the study was executed in highly experienced FLS centers, the transferability of our cost-effectiveness analysis to other FLS centers is uncertain as the effect of MCAI could be different in various healthcare systems in which osteoporosis incidence or FLS costs are also different. Therefore, if FLS care is promoted sufficiently, MCAI could offer further post-fracture care improvement. Moreover, besides the insignificant results on AOM persistence in the ICON study, the ICON data available also included a relatively short period of one year. This could have influenced the persistence data when simulating for lifetime impact as oral bisphosphonates are usually prescribed for five consecutive years and further subsequent fractures that are prevented could be simulated more accurately. Consequently, long-term side effects were not included past the one-year data although limited effects of adverse events on the cost-effectiveness of AOM are expected [45]. Lastly, this model did not include changes made by the patients in AOM treatment or its efficacy due to its complexity in modeling.

Moreover, this study solely included patients who were candidates for oral bisphosphonate treatment, therefore lacking generalizability for all patients eligible for AOM. A previous Dutch study suggested that oral bisphosphonates account for about 70% of patients that initiate AOM treatment with a recent fracture over the age of 50 years old [5]. Additional factors that could have contributed to the results include the effect of comorbidities or medical costs related to surgery needed for fractures. Nonetheless, the difference between MCAI and UC in AOM persistence after 6 months and one year could indicate the value of the MCAI.

Although our study demonstrates dominance of the MCAI, further research should focus on personalized adherence support as the MCAI could not work for all patients and impact on individual patients should be investigated. Therefore, future research on cost-effectiveness of adherence-enhancing interventions should include different subgroups, as effect of adherence tools could be different, by example between patients with limited and adequate health literacy. Due to the relatively little consequent fracture prevention, further research should also focus long-term follow-up of MCAI to analyze whether the MCAI could possibly lead to suboptimal decision-making over a longer period of time, particularly in cases where osteoporosis is already at a critical stage. Lastly, not only should research elaborate on adherence tools in subgroups but also on increasing FLS attendance rates (as this is currently 51% in the same center as some of our patients attended) [53], and estimating a threshold of AOM persistence and adherence that may reach the plateau of healthcare of patients with a recent fracture and eligible for AOM.

In conclusion, this study assessed the costs and outcomes of a real-life MCAI implemented in high quality FLS centers. Despite a small effect on persistence, the results suggest that MCAI was dominant compared to usual care and represents therefore a good allocation of resources in FLS care, depending however on extra costs with FLS.

Supplementary Material

Supplemental Material

Declaration of Interest

A Boonen received research grants from Abbvie and honoraria for lectures or consultation from Pfizer, Novartis, UCB, Abbvie and Galapagos; all to her department. M Hiligsmann received research grants from Radius Health and Angelini Pharma (paid to institution), lecture fees from Mylan Pharmaceuticals and IBSA (paid to institution) and was grant advisor for Pfizer (paid to institution). The department of JP. van den Bergh has received consultancy fees from UCB and Amgen and research grants from the Novo Nordisk Foundation and UCB. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Author contributions

A Boonen, C E. Wyers, J P. van den Bergh, and M Hiligsmann contributed to the study conception and design. L Maas, N Li, and M Hiligsmann contributed to the interpretation of findings. L Maas drafted the manuscript. All authors reviewed the manuscript critically for important intellectual content. All authors approved the final version of the manuscript for submission.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14737167.2024.2366439.
==== Refs
References

Papers of special note have been highlighted as either of interest (•) or of considerable interest (••) to readers.

1. Our network: International osteoporosis foundation; [cited 2024 Feb 2]. Available from: https://www.osteoporosis.foundation/our-network
2. Li N, Hiligsmann M, Boonen A, et al. The impact of fracture liaison services on subsequent fractures and mortality: a systematic literature review and meta-analysis. Osteoporos Int. 2021;32 (8 ):1517–1530. doi: 10.1007/s00198-021-05911-9 33829285
3. Wu CH, Kao IJ, Hung WC, et al. Economic impact and cost-effectiveness of fracture liaison services: a systematic review of the literature. Osteoporos Int. 2018;29 (6 ):1227–1242. doi: 10.1007/s00198-018-4411-2 29460102
4. Lüthje P, Nurmi-Lüthje I, Tavast N, et al. Evaluation of minimal fracture liaison service resource: costs and survival in secondary fracture prevention—a prospective one-year study in South-Finland. Aging Clin Exp Res. 2021;33 (11 ):3015–3027. doi: 10.1007/s40520-021-01826-x 33811622
5. Li N, van den Bergh JP, Boonen A, et al. Cost-effectiveness analysis of fracture liaison services: a Markov model using Dutch real-world data. Osteoporos Int. 2023;35 (2 ):293–307. doi: 10.1007/s00198-023-06924-2 37783759
•• The model was based on the model presented in this study.

6. Hiligsmann M, Boonen A, Rabenda V, et al. The importance of integrating medication adherence into pharmacoeconomic analyses: the example of osteoporosis. Expert Rev Pharmacoecon Outcomes Res. 2012;12 (2 ):159–166. doi: 10.1586/erp.12.8 22458617
7. Saunders H, Sujic R, Bogoch ER, et al. Cost-utility analysis of the Ontario fracture screening and prevention program. JBJS. 2021;103 (13 ):1175–1183. doi: 10.2106/JBJS.20.00795
8. Senay A, Fernandes JC, Delisle J, et al. Patient healthcare trajectory and its impact on the cost-effectiveness of fracture liaison services. Journal Of Bone And Mineral Research. 2021;36 (3 ):459–468. doi: 10.1002/jbmr.4216 33484586
9. Klop C, Welsing PM, Elders PJ, et al. Long-term persistence with anti-osteoporosis drugs after fracture. Osteoporos Int. 2015;26 (6 ):1831–1840. doi: 10.1007/s00198-015-3084-3 25822104
10. McHorney CA, Schousboe JT, Cline RR, et al. The impact of osteoporosis medication beliefs and side-effect experiences on non-adherence to oral bisphosphonates. Curr Med Res Opin. 2007;23 (12 ):3137–3152. doi: 10.1185/030079907X242890 17988435
11. Lewiecki EM. Risk communication and shared decision making in the care of patients with osteoporosis. J Clin Densitom. 2010;13 (4 ):335–345. doi: 10.1016/j.jocd.2010.06.005 20663701
12. Politi MC, Wolin KY, Légaré F. Implementing clinical practice guidelines about health promotion and disease prevention through shared decision making. J Gen Intern Med. 2013;28 (6 ):838–844. doi: 10.1007/s11606-012-2321-0 23307397
13. Agoritsas T, Heen AF, Brandt L, et al. Decision aids that really promote shared decision making: the pace quickens. BMJ. 2015;350 :g7624. doi: 10.1136/bmj.g7624 25670178
14. Cranney A, O’Connor AM, Jacobsen MJ, et al. Development and pilot testing of a decision aid for postmenopausal women with osteoporosis. Patient Educ Couns. 2002;47 (3 ):245–255. doi: 10.1016/S0738-3991(01)00218-X 12088603
15. Hiligsmann M, Bours SP, Boonen A. A review of patient preferences for osteoporosis drug treatment. Curr Rheumatol Rep. 2015;17 (9 ):61. doi: 10.1007/s11926-015-0533-0 26286178
16. Hiligsmann M, Ronda G, van der Weijden T, et al. The development of a personalized patient education tool for decision making for postmenopausal women with osteoporosis. Osteoporos Int. 2016;27 (8 ):2489–2496. doi: 10.1007/s00198-016-3555-1 27048388
• The PDA, part of the MCAI, was based on the PDA presented in this study.

17. LeBlanc A, Wang AT, Wyatt K, et al. Encounter decision aid vs. clinical decision support or usual care to support patient-centered treatment decisions in osteoporosis: the osteoporosis choice randomized trial II. PLOS ONE. 2015;10 (5 ):e0128063. doi: 10.1371/journal.pone.0128063 26010755
18. Pencille LJ, Campbell ME, Van Houten HK, et al. Protocol for the osteoporosis choice trial. A pilot randomized trial of a decision aid in primary care practice. Trials. 2009;10 (1 ):113. doi: 10.1186/1745-6215-10-113 20003299
19. Charles C, Gafni A, Whelan T. Decision-making in the physician–patient encounter: revisiting the shared treatment decision-making model. Social Science & Medicine. 1999;49 (5 ):651–661. doi: 10.1016/S0277-9536(99)00145-8 10452420
20. Paskins Z, Torres Roldan VD, Hawarden AW, et al. Quality and effectiveness of osteoporosis treatment decision aids: a systematic review and environmental scan. Osteoporos Int. 2020;31 (10 ):1837–1851. doi: 10.1007/s00198-020-05479-w 32500301
21. Hiligsmann M, McGowan B, Bennett K, et al. The clinical and economic burden of poor adherence and persistence with osteoporosis medications in Ireland. Value Health. 2012;15 (5 ):604–612. doi: 10.1016/j.jval.2012.02.001 22867768
22. Penton H, Hiligsmann M, Harrison M, et al. Potential cost-effectiveness for using patient decision aids to guide osteoporosis treatment. Osteoporos Int. 2016;27 (9 ):2697–2707. doi: 10.1007/s00198-016-3596-5 27155885
23. Maas L, Raskin N, van Onna M, et al. Development and usability of a decision aid to initiate anti-osteoporosis medication treatment in patients visiting the fracture liaison service with a recent fracture. Osteoporos Int. 2023;35 (1 ):69–79. doi: 10.1007/s00198-023-06906-4 37733067
24. Cornelissen D, Boonen A, Evers S, et al. Improvement of osteoporosis care organized by nurses: ICON study - protocol of a quasi-experimental study to assess the (cost)-effectiveness of combining a decision aid with motivational interviewing for improving medication persistence in patients with a recent fracture being treated at the fracture liaison service. BMC Musculoskelet Disord. 2021;22 (1 ):913. doi: 10.1186/s12891-021-04743-2 34715838
25. Maas L, Hiligsmann M, Wyers CE, et al. Effect of combining a patient decision aid with motivational interviewing on adherence and quality of shared decision making among patients with a recent fracture attending a fracture liaison service and requiring anti-osteoporosis treatment – a quasi-experimental intervention study [abstract] In: WCO-IOF-ESCEO abstract book. 2024;Abstract number P765.
26. Hiligsmann M, Reginster JY, Tosteson ANA, et al. Recommendations for the conduct of economic evaluations in osteoporosis: outcomes of an experts’ consensus meeting organized by the European society for clinical and economic aspects of osteoporosis, Osteoarthritis and musculoskeletal diseases (ESCEO) and the US branch of the international osteoporosis foundation. Osteoporos Int. 2019;30 (1 ):45–57. doi: 10.1007/s00198-018-4744-x
27. Husereau D, Drummond M, Augustovski F, et al. Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. J Med Econ. 2022;25 (sup1 ):1–7. doi: 10.1080/13696998.2021.2014721
28. Genant HK, Wu CY, van Kuijk C, et al. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res. 1993;8 (9 ):1137–1148. doi: 10.1002/jbmr.5650080915 8237484
29. Stacey D, Volk RJ. The international patient decision aid standards (IPDAS) collaboration: evidence update 2.0. Med Decis Making. 2021;41 (7 ):729–733. doi: 10.1177/0272989X211035681 34416841
30. Versteegh M, Knies S, Brouwer W. From good to better: new Dutch guidelines for economic evaluations in healthcare. Pharmacoeconomics. 2016;34 (11 ):1071–1074. doi: 10.1007/s40273-016-0431-y 27613159
31. Centraal Bureau Statistiek. Mortality rate. 2023 [cited 2024 Feb 20]. Available from: https://opendata.cbs.nl/
32. Haentjens PM, Colón-Emeric CS, Vanderschueren D, et al. Meta-analysis: excess mortality after hip fracture among older women and men. Ann Intern Med. 2010;152 (6 ):380–390. doi: 10.7326/0003-4819-152-6-201003160-00008 20231569
•• Description of the development and usability of the PDA, part of the MCAI, of this study.

33. Bliuc D, Nguyen ND, Milch VE, et al. Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. JAMA. 2009;301 (5 ):513–521. doi: 10.1001/jama.2009.50 19190316
34. Hiligsmann M, Reginster J-Y. Cost effectiveness of denosumab compared with oral bisphosphonates in the treatment of post-menopausal osteoporotic women in Belgium. Pharmacoeconomics. 2011;29 (10 ):895–911. doi: 10.2165/11539980-000000000-00000 21692551
35. Geel T, Helden S, Geusens PP, et al. Clinical subsequent fractures cluster in time after first fractures. Ann Rheumatic Dis. 2009;68 :99–102. doi: 10.1136/ard.2008.092775
36. Lötters FJB, van den Bergh JP, de Vries F, et al. Current and future incidence and costs of osteoporosis-related fractures in the Netherlands: combining claims data with BMD measurements. Calcif Tissue Int. 2016;98 (3 ):235–243. doi: 10.1007/s00223-015-0089-z 26746477
37. Meerding WJ, Looman CW, Essink-Bot ML, et al. Distribution and determinants of health and work status in a comprehensive population of injury patients. The Journal Of Trauma: Injury, Infection, And Critical Care. 2004;56 (1 ):150–161. doi: 10.1097/01.TA.0000062969.65847.8B
38. Average annual salary in the Netherlands in 2022, by age: statista research department. 2023 [cited 2024 Feb 8]. Available from: https://www.statista.com/statistics/538406/average-annual-salary-in-the-nethe rlands-by-age/
39. Li N, van Oostwaard M, van den Bergh JP, et al. Health-related quality of life of patients with a recent fracture attending a fracture liaison service: a 3-year follow-up study. Osteoporosis Int. 2022;33 (3 ):577–588. doi: 10.1007/s00198-021-06204-x
40. Svedbom A, Borgstöm F, Hernlund E, et al. Quality of life for up to 18 months after low-energy hip, vertebral, and distal forearm fractures—results from the ICUROS. Osteoporosis Int. 2018;29 (3 ):557–566. doi: 10.1007/s00198-017-4317-4
41. Söreskog E, Lindberg I, Kanis JA, et al. Cost-effectiveness of romosozumab for the treatment of postmenopausal women with severe osteoporosis at high risk of fracture in Sweden. Osteoporosis Int. 2021;32 (3 ):585–594. doi: 10.1007/s00198-020-05780-8
42. Cummings SR, San Martin J, McClung MR, et al. Denosumab for prevention of fractures in postmenopausal women with osteoporosis. N Engl J Med. 2009;361 (8 ):756–765. doi: 10.1056/NEJMoa0809493 19671655
43. National Institute for Health and ClinicalExcellence. Systematic reviews of clinical effectiveness prepared for the guideline ‘Osteoporosis: assessment of fracture risk and the prevention of osteoporotic fractures in individuals at high risk’. National Collaborating Centre for Nursing and Supportive Care; 2008. https://www.nice.org.uk/guidance/cg146/documents/osteoporosis-evidence-reviews2
44. ALENDRONINEZUUR [Internet]. Medicijnkosten.nl. 2023. cited 2024 Jan 24]. Available from: https://www.medicijnkosten.nl/
45. Hiligsmann M, Williams SA, Fitzpatrick LA, et al. Cost-effectiveness of sequential treatment with abaloparatide vs. teriparatide for United States women at increased risk of fracture. Semin Arthritis Rheum. 2019;49 (2 ):184–196. doi: 10.1016/j.semarthrit.2019.01.006 30737062
46. Nederlandse Zorgautoriteit. Open data van de Nederlandse Zorgautoriteit. 2024 [cited 2024 Feb 18]. Available from: https://www.opendisdata.nl/msz/zorgproduct/131999067
47. Kanis JA, Oden A, Johnell O, et al. Excess mortality after hospitalisation for vertebral fracture. Osteoporosis Int. 2004;15 (2 ):108–112. doi: 10.1007/s00198-003-1516-y
48. Kanis JA, Oden A, Johnell O, et al. The components of excess mortality after hip fracture. Bone. 2003;32 (5 ):468–473. doi: 10.1016/S8756-3282(03)00061-9 12753862
49. Nederland Z. Richtlijn voor het uitvoeren van economische evaluaties in de gezondheidszorg. 2024 [cited 2024 Jan 24]. Available from: https://www.zorginstituutnederland.nl/over-ons/publicaties/publicatie/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg
50. Vijgen S, van Heesch F, Ziektelast MO de Praktijk I. 2018. p. 1–34.
51. Reckers-Droog VT, van Exel NJA, Brouwer WBF. Looking back and moving forward: on the application of proportional shortfall in healthcare priority setting in the Netherlands. Health Policy. 2018;122 (6 ):621–629. doi: 10.1016/j.healthpol.2018.04.001 29703652
52. ZonMW Kennisplatform Preventie (KPP). Preventie op waarde schatten: advies technische werkgroep kosten en baten van preventie. 2023.
53. Vranken L, de Bruin IJA, Driessen AHM, et al. Decreased mortality and subsequent fracture risk in patients with a major and hip fracture after the introduction of a fracture liaison service: a 3‐year follow‐up survey. Journal Of Bone And Mineral Research. 2022;37 (10 ):2025–2032. doi: 10.1002/jbmr.4674 36087016
