
==== Front
Ophthalmol Ther
Ophthalmol Ther
Ophthalmology and Therapy
2193-8245
2193-6528
Springer Healthcare Cheshire

39106031
999
10.1007/s40123-024-00999-8
Original Research
Economic Assessment in Resource-Constrained Systems: Individual-Level Simulation Model in Wet Age-Related Macular Degeneration and Diabetic Macular Oedema
http://orcid.org/0000-0002-4888-7419
Li Tara 1
http://orcid.org/0000-0002-5743-2184
Berdunov Vladislav vlad.berdunov@putassoc.com

1
Hamilton Robin 2
Rojas Yaneth Gil 1
Bührer Christian 3
http://orcid.org/0000-0002-2308-7837
Cox Oliver 3
Postema Roelien 1
Bagijn Marloes 3
1 Putnam, 22-24 Torrington Place, Fitzrovia, London, WC1E 7HJ UK
2 https://ror.org/03tb37539 grid.439257.e 0000 0000 8726 5837 Moorfields Eye Hospital, London, UK
3 grid.417570.0 0000 0004 0374 1269 F. Hoffmann-La Roche Ltd, Basel, Switzerland
6 8 2024
6 8 2024
10 2024
13 10 25772597
24 5 2024
9 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Introduction

Cost-effectiveness analyses typically ignore healthcare system resource constraints. Ophthalmology is affected by resource constraints because of increasing disease prevalence and the use of resource-intensive treatments. This study evaluated the impact of resource constraints on the cost-effectiveness of faricimab 6 mg, compared with aflibercept 2 mg and ranibizumab biosimilar 0.5 mg, for treating wet age-related macular degeneration (wAMD) or diabetic macular oedema (DMO) over a 5-year horizon.

Methods

A microsimulation model estimated the impact of resource constraints on patients visits, delays, costs and quality-adjusted life-year (QALY) losses due to treatment delays at a typical UK National Health Service eye hospital treating 1500 patients with wAMD and 500 patients with DMO. Patient characteristics, treatment regimens and treatment intervals were informed using published literature and expert opinion. Resource constraint was represented by limiting the number of available intravitreal injection appointments per week, with growing demand caused by rising disease prevalence. The model compared outcomes across three scenarios; each scenario involved treating all patients with one of the three treatments.

Results

Over 5 years, in a resource-constrained hospital, compared with aflibercept, faricimab use resulted in the avoidance of 12,596 delays, saved GBP/£15,108,609 in cost and avoided the loss of 60.06 QALYs. Compared with ranibizumab biosimilar, faricimab use resulted in the avoidance of 18,910 delays, incurred £2,069,088 extra cost and avoided the loss of 105.70 QALYs, resulting in an incremental cost-effectiveness ratio of £19,574/QALY.

Conclusions

Accounting for resource constraints in health economic evaluation is crucial. Emerging therapies that are more durable and require less frequent clinic visits can reduce treatment delays, leading to improved QALY outcomes and reduced burden on healthcare systems. Faricimab reduced the number of delayed injections, leading to improved QALY outcomes for patients in a healthcare system with resource constraints. Faricimab is cost-saving when compared with aflibercept and cost-effective when compared with ranibizumab biosimilar.

Graphical Abstract

Keywords

Resource constraints
Capacity constraints
Wet age-related macular degeneration
Diabetic macular oedema
Simulation model
http://dx.doi.org/10.13039/100007013 F. Hoffmann-La Roche issue-copyright-statement© Springer Healthcare Ltd., part of Springer Nature 2024
==== Body
pmcKey Summary Points

Why carry out this study?	
In ophthalmology, it may be particularly important to consider the impact of resource constraints on patients and health systems when assessing the value of new treatments.	
This case study presents a patient-level simulation model that estimates the number of patient visits, delays, treatment costs and lost quality-adjusted life years (QALYs) associated with delayed treatments in patients with wet age-related macular degeneration (wAMD) or diabetic macular oedema (DMO).	
What was learned from this study?	
Results from this case study showed that using faricimab reduced the number of delays compared with using aflibercept or ranibizumab biosimilar, leading to improved QALY outcomes for patients with wAMD or DMO in a healthcare system with resource constraints.	
Emerging therapies that are more durable and require less frequent clinic visits, such as faricimab, can reduce treatment delays, leading to net cost savings from the healthcare payer perspective and improved QALY outcomes for patients with wAMD or DMO.	

Digital Features

This article is published with digital features, including a graphical abstract, to facilitate understanding of the article. To view digital features for this article, go to 10.6084/m9.figshare.26218133.

Introduction

In many healthcare systems, cost-effectiveness and budget impact analyses of a new treatment compared with the existing treatment options (standard of care) are the cornerstones of the health technology assessment (HTA) decision-making process. Decisions on whether new treatments are reimbursed by payers are typically based on affordability and budgetary considerations. Although these analyses address comparative efficiency, the impact of resource constraints in a healthcare system on the implementation of new technologies is often not part of the decision-making process and has been largely ignored [1]. Healthcare resources, such as beds, doctors and nurses, are constrained, but most economic evaluations assume all resources required by a new technology and existing treatment options are immediately available following the adoption of the new technology, with the potential impact of resource constraints not routinely captured in economic evaluations. Failing to consider resource constraints may lead to low levels of uptake or impracticality of implementing a new technology. In addition, not considering resource constraints could lead to inefficient use of overall healthcare resources, resulting in long waiting lists and suboptimal population health outcomes.

While many disease areas can be affected by resource constraints, ophthalmology is one of the areas that is most affected because of an increasing prevalence of retinal conditions and increasing utilisation of resource-intensive treatments [2, 3]. In addition to the published data, there is a huge hidden backlog for follow-up appointments because the published figure only includes those waiting for the first appointment. Permanent harm, such as avoidable visual loss from delays to care, is ninefold more likely to happen in follow-up patients than in new patients [4]. It is estimated that every month approximately 22 people in the UK suffer from severe or permanent sight loss due to delays caused by the capacity challenges the UK National Health System (NHS) currently faces [5]. NHS England revealed that there were 551 reports of sight loss due to delayed appointments between 2019 and 2023 in the National Reporting and Learning System [6].

Wet age-related macular degeneration (wAMD) and diabetic macular oedema (DMO) are both chronic and debilitating conditions with a substantial impact on the quality of life. The prevalence of late age-related macular degeneration (AMD) in the UK is 2.4% among people aged ≥ 50 years and 4.8% among people aged ≥ 65 years [7]. Over time, about one in 15 people with diabetes may develop DMO [8]. With an ageing population and increasing prevalence of diabetes, a key prognostic factor, the incidence of AMD and DMO is predicted to rise substantially [9, 10]. Intravitreal therapy, the first-line treatment option for AMD and DMO, requires anti-vascular endothelial growth factor (anti-VEGF) injections as frequently as every 4 weeks and regular eye examinations at a specialist eye clinic for many years [11, 12]. This imposes a considerable burden on specialist eye clinics, which are subject to considerable resource constraints (such as the limited number of injection appointments that are available for patients, especially in the post-COVID-19 era). Between 2009 and 2019, a single tertiary care hospital in the UK saw an 11-fold increase in the number of anti-VEGF injections administered, totalling 44,924 injections delivered in 2019. This number is predicted to reach 83,000 in the year 2029 [13]. The increasing demand for anti-VEGF injections tests NHS resources and contributes to delays in initiating and providing treatments, ultimately resulting in poor vision outcomes [14].

Currently, four agents are recommended by the UK National Institute for Health and Care Excellence (NICE) as treatment options for treating AMD and DMO: ranibizumab [15, 16], aflibercept [17, 18], brolucizumab [19, 20] and faricimab [12, 21]. Brolucizumab was excluded from the present case study because it may be used as a second-line therapy, whereas the other three agents are first-line therapies [20]. Different from other agents, faricimab is a combined-mechanism medication with effects on both VEGF A and angiopoietin-2. Although the efficacy of anti-VEGF agents is well established, the majority of patients with AMD and DMO treated with ranibizumab and aflibercept would need intravitreal injections every 4 to 8 weeks. Dosing intervals may be extended to 12 weeks for some patients being treated with ranibizumab and to 16 weeks for some patients being treated with aflibercept, but the proportion of these patients is relatively low. With faricimab, the dosing interval may be extended to 16 weeks in > 60% of the patients.

Considering delays in treatment is known to lead to vision loss in AMD and DMO [22, 23], it is essential to ensure injections can be provided promptly, especially in the context of resource constraints. Early real-world evidence suggests a promising effect of faricimab on reducing resource use and relieving resource constraints [24, 25]. In the NICE productivity initiative, faricimab was recognised as a productivity opportunity because it is associated with a reduction in the number of both clinical appointments and outpatient injections [26]. Emerging sight-saving therapies such as faricimab, associated with more durable effects and reduced resource use (i.e. longer treatment intervals between 2 injections), can help to address the negative impact of resource constraints on vision outcomes due to potential treatment delays. However, traditional economic evaluation methods used in HTA which ignore the impact of resource constraints are unlikely to capture the true value of more durable therapies within the reality of the healthcare system.

New approaches to modelling treatments which alleviate the strain on resource-constrained systems may be required. This study aims to quantify the extent of resource constraints and their impact on the cost-effectiveness of treatments for wAMD and DMO as a case study, using an individual-level simulation model.

Methods

An individual-level simulation model was developed in Microsoft Excel (Microsoft Corp., Redmond, WA, USA) to estimate the impact of limited resources in the ophthalmology service at a hypothetical NHS eye hospital. Patients and their required visits to the hospital were simulated based on pre-specified model inputs, including patient characteristics, treatment intervals, hospital capacity and maximum allowed delay time. The details of these inputs are described in the following sections. The model used a weekly time cycle during which all patient-hospital interactions were simulated (see section Booking Schedule for details). The model measured the number of patient visits, the number and duration of delays, the cost of treatment for wAMD and DMO and the loss of quality-adjusted life years (QALYs) due to delayed treatments. The model simulated and compared the outcomes across three hypothetical scenarios; in each scenario, all patients are treated with one of the three treatments, namely faricimab, aflibercept or ranibizumab biosimilar. To the best of our knowledge, this is the first cost-effectiveness study of pharmacological treatments for wAMD and DMO that has considered both demand and supply constraints.

Patient Characteristics

The model simulated a cohort of 1500 patients with wAMD and 500 patients with DMO, representing the number of patients at a typical NHS eye hospital, over a 5-year time horizon. New patients were added to the model based on a prespecified annual growth rate of 2.7% for wAMD and 2.6% for DMO [27, 28]. Based on a study conducted by Giocanti-Auregan and colleagues, 36% of patients with wAMD and 48% of patients with DMO would have bilateral disease [29].

Treatment Intervals

Treatment intervals were informed using data from randomised controlled trials and long-term studies of faricimab, aflibercept and ranibizumab [30–36], other literature [17, 18] and expert opinion obtained from a series of extensive discussions with an NHS ophthalmologist, Dr. Robin Hamilton, who is a consultant at the Moorfield Eye Hospital London (UK).

All patients were assumed to be on a treat-and-extend treatment regimen for all treatments. Treatment intervals for faricimab were informed based on the TENAYA and LUCERNE trials for wAMD [31] and the YOSEMITE and RHINE trials for DMO [36]. For DMO, treatment frequency for aflibercept and ranibizumab biosimilar was assumed to be the same as that for ranibizumab, informed by the TREX-DME study [34]. This was because the proportion of patients on a 12-week interval in the ranibizumab TREX-DME study (43.0%) was similar to that in the aflibercept VIOLET study (40.8%) [30]. This assumption has been validated by a clinical expert. In the case of wAMD, the intervals for aflibercept and ranibizumab were informed using the ARIES [35] and Kertes et al. [33] trials, respectively.

Table 1 presents the treatment regimen and interval inputs data used in the model. With faricimab, four doses at a 4-week interval were given to patients during the loading period for both wAMD and DMO; with aflibercept and ranibizumab biosimilar, three doses or five doses were given to patients with wAMD or DMO, respectively, at a 4-week interval. The model further assumes that, following the loading phase, 22% of patients treated for wAMD with faricimab would reach an 8-week injection interval, 15% would reach a 12-week interval and 63% would reach a 16-week interval; meanwhile, 22% of patients treated for DMO with faricimab would reach an 8-week interval, 16% would reach a 12-week interval and 62% would reach a 16-week interval. With aflibercept, 57% of patients treated for wAMD would be on an 8-week treatment interval, 17% would be on a 12-week interval and 26% would be on a 16-week interval; and 28% of patients treated for DMO would be on a 4-week treatment interval, 29% would be on an 8-week interval and 43% would be on a 12-week interval. For ranibizumab biosimilar, 26% of patients with wAMD would be on a 4-week interval, 31% would be on an 8-week interval and 43% would be on a 12-week interval. Table 1 Treatment interval inputs

Condition and treatment interval	Faricimab, 100% T&E	Aflibercept, 100% T&E	Ranibizumab biosimilar, 100% T&E	
wAMD	
 Source	TENAYA and LUCERNE [30]	ARIES [34]	Kertes et al. [32]	
 Loading; 4-week interval	4 doses	3 doses	3 doses	
 4-week interval, % of patients	NA	NA	26.0	
 8-week interval, % of patients	22.0	57.0	31.0	
 12-week interval, % of patients	15.0	17.0	43.0	
 16-week interval, % of patients	63.0	26.0	NA	
DMO	
 Source	YOSEMITE and RHINE [35]	TREX-DME [33]	TREX-DME [33]	
 Loading; 4-week interval	4 doses	5 doses	5 doses	
 4-week interval, % of patients	NA	28.0	28.0	
 8-week interval, % of patients	22.0	29.0	29.0	
 12-week interval, % of patients	16.0	43.0	43.0	
 16-week interval, % of patients	62.0	NA	NA	
DMO Diabetic macular oedema, NA not available, T&E treat and extend, wAMD wet age-related macular degeneration

It is worth noting that, for faricimab in DMO, the proportions of patients requiring treatment every 4 weeks and every 8 weeks were combined, and all were assumed to be on an 8-week treatment interval in order to minimise model complexity. The same assumption applied for aflibercept in wAMD. An alternative assumption, where all patients on 4- and 8-week treatment intervals were assumed to be on a 4-week treatment interval, was tested in a scenario analysis. All patients were assumed to be receiving treatment throughout the entire model horizon of 5 years, meaning no patients would discontinue treatment. Treatment discontinuation was modelled as a scenario analysis.

Resource Constraint

An explicit resource constraint was represented by setting a limit on the number of intravitreal injection appointments per week, with growing demand from rising disease prevalence. Clinic capacity was calculated as the product of the number of sessions and clinics occurring each week and the number of patient visits that can be provided in each session. Based on expert opinion, 20 four-hour injection sessions were an appropriate estimate for weekly capacity. However, NHS staff typically work 44 weeks instead of 52 weeks a year. Therefore, 20 sessions were reduced to 17 to account for staff holidays. Within each 4-hour injection session, 16 injections can be delivered [37]. It was assumed that 77.6% injection capacity was offered to patients with wAMD and DMO at this hypothetical NHS eye hospital to allow for capacity use for other health conditions [13]. This capacity was used to determine when demand exceeded capacity, and then either the patient visit was delayed or provided as an out-of-hours (OOH) service.

Out-of-Hours Appointment and Treatment Delays

Based on baseline patient characteristics and treatment intervals, modelled patients were placed on a waiting list for an available clinic appointment until the maximum allowed waiting time was reached, after which an OOH appointment was provided at the hospital. A delay occurred when the demand for clinic visits in a given week exceeded the available routine capacity. Because initial assessment and treatment visits are generally considered to be high priority and delays should ideally be avoided, it was assumed in the model base case that the maximum allowed delay was 2 weeks for first appointments for wAMD (as per NICE’s commissioning guidance [38]), 4 weeks for wAMD follow-up appointments and DMO first appointments and 6 weeks for DMO follow-up appointments. This assumption has been validated by a clinical expert. In addition, the impact of shorter allowable delays (i.e. 1 week for first appointments for wAMD, 3 weeks for wAMD follow-up appointments and DMO first appointments, 5 weeks for DMO follow-up appointments) has been tested using scenario analyses.

The cost of OOH appointments was assumed to be twofold higher than that of routine appointments. A scenario analysis was conducted assuming the cost of an OOH appointment was threefold higher than that of a routine appointment.

Booking Schedule

A key assumption of the model was that the clinic maintained a booking schedule containing the week of next visit for every patient treated by the clinic. The model used the booking schedule property to track and schedule each patient’s next clinic visit. During the initiation phase, every patient under the care of the clinic was assigned to a slot in the booking schedule based on their assigned treatment and regimen, as well as the clinic’s capacity. As the simulation progressed, the booking schedule determined the events that would occur during each week. After a patient completed a clinic visit, they were rebooked to a new slot in the schedule. The schedules under the alternative scenarios of worlds with different treatments diverged because patients were on different treatment intervals.

The approach to scheduling patient visits is illustrated in Fig. 1. When a patient was simulated and passed to the booking method of the clinic, the initial step involved determining the patient’s target booking week. This is the week in which the visit ought to take place based on the patient’s regimen. The next step was to check whether there was capacity in the target week to provide this patient visit; if so, the booking would be assigned a slot within routine hours. If the capacity limit was reached in that week, then the visit could either be conducted OOH or it could be delayed to a later week. If the limit on waiting times had not been reached, then the booking scheduler moved to the next week and repeated the above process. If the limit on waiting times had been reached, the booking was assigned an OOH visit. OOH slots were assumed to be unlimited.Fig. 1 Flow diagram of booking schedule's process for assigning patient appointments

Resource Use and Costs

Ophthalmology clinic visits were categorised according to types that are intended to broadly capture the differences in terms of resources required. Resources associated with a baseline assessment (i.e. treatment initiation) included a first-time consultation, optical coherence tomography, fundus fluorescein angiography and drug acquisition and administration costs. All subsequent appointments were assumed to involve a follow-up consultation, optical coherence tomography and drug acquisition and administration costs. The cost of baseline assessment and follow-up appointment was GBP/£576.4 and £383.8, respectively. The list price for faricimab, aflibercept and ranibizumab biosimilar was £857.0, £816.0 and £523.5, respectively. Unit costs were sourced from the National Schedule of NHS Costs [39], and drug acquisition costs were based on prices listed in the British National Formulary [40]. No discounting was applied because of the short time horizon in the model. Costs associated with vision loss were not included in the case study.

Health-Related Quality of Life

The impact of treatment delays was measured in QALYs by combining visual acuity and associated utility, based on the assumption that treatment delays would lead to worsened visual acuity as well as delayed treatment benefit [41]. It was assumed that a treatment delay of 1 week would lead to a vision loss of 0.23 Early Treatment Diabetic Retinopathy Study (ETDRS) letters as well as the loss of 0.12 ETDRS letters vision gain had the treatment been given in time. Based on the algorithm (model 1 + 2) published in Hodgson and colleagues [42], which estimated a regression model to correlate visual acuity and utility, we conservatively assumed that a change of five ETDRS letters is associated with a utility change of 0.0160 using the smallest change in utility value between any two visual acuity health states. The total utility decrement due to treatment delays was calculated as the product of the number of delays, estimated average delay duration and utility change associated with a 0.35 change in ETDRS letters.

Outcomes

Total number of clinic attendances, total number of injections given, total cost and total QALY loss due to treatment delays were estimated for scenarios in which all patients were treated with each treatment. Analysis results were reported using incremental costs, incremental cost-effectiveness ratio (ICER) and net monetary benefit (NMB) for one scenario with all patients treated using faricimab versus all patients treated with aflibercept and for another scenario with all patients treated with faricimab versus all patients treated with ranibizumab biosimilar.

Uncertainty

Scenario analyses were performed to assess the impact of key parameters on the base case results (Table 2). The purpose of scenario analyses was to explore different assumptions in demand- and supply-side factors within the system. On the supply side, this involved changes in appointment delays; on the demand side, it included adjustments to intervals between injections, discontinuation of treatment or an increase in the number of patients accessing the service. A scenario that assumed the absence of capacity constraints allowed a comparison of the base case with a standard cost-effectiveness model, which does not explicitly consider capacity constraints. We explored uncertainties in cost and utility values by testing alternative costs and utility data sources and assumptions. Table 2 List of scenarios

Scenario number	Description	Base case	Scenario	Source	
1	Discontinuation	Do not model discontinuation	Model discontinuation using data sources used in previous NICE appraisals	NICE TA294 [17] and TA346 [18]	
2	Regimen and treatment interval	The base case model combines the proportions of patients treated Q4W (9%) and Q8W (13%) with faricimab and assumed 22% on Q8W after loading for DMO

For wAMD, the model combines the proportions of patients treated Q4W (10%) and Q8W (47%) with aflibercept and assumed 57% on Q8W after loading

	Combining proportions of patients treated Q4W (X%) and Q8W (Y%). Assumed X + Y% on Q4W after loading in order to test the impact of more capacity-demanding treatment intervals	RCTs	
3	Cost multiplier for out-of-hours appointments	2	3	Assumption	
4	Utility value for visual acuity per treatment delay	Source for relationship between VA loss due to treatment delays and utility: Hodgson et al. model 1 + 2 for BSE and WSE independently [42]	Used algorithm in Czoski-Murray et al. [63]	Czoski-Murray et al. [43]	
5	Maximum delay time allowed	For wAMD: initial appointment, 2 weeks; follow-up appointment, 4 weeks

For DMO: initial appointment, 4 weeks; follow-up appointment, 6 weeks

	Decrease maximum delay time allowed by 1 week	Assumption	
6	Patient population growth	2.7% annually	Double the rate	Expert opinion	
BSE Better-seeing eye, DMO diabetic macular oedema, NICE UK National Institute for Health and Care Excellence, Q4W every 4 weeks, Q8W every 8 weeks, RCT randomised controlled trial, VA visual acuity, wAMD wet age-related macular degeneration, WSE worse-seeing eye

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Results

Base Case

As estimated by the model, in the comparison between faricimab and aflibercept, for 2000 simulated patients with wAMD or DMO over a 5-year period there was a decrease of 15,975 (− 20.81%) in the number of injections when all patients were treated with faricimab. In the comparison between faricimab and ranibizumab biosimilar, the reduction was 20,824 (− 25.56%). Additionally, using faricimab reduced almost all delays compared with using aflibercept (99.97% reduction) and ranibizumab biosimilar (99.99% reduction) (see Fig. 2; Table 3). The use of faricimab was dominant (total cost savings of − £15,108,609, 60.06 QALYs gained) when compared with aflibercept (NMB £16,309,863). Compared with ranibizumab biosimilar, faricimab had an incremental cost of £2,069,088 and provided 105.7 additional QALYs. This yielded an ICER per QALY gained of £19,574 (NMB: £45,011), which is below the NICE threshold for cost-effectiveness of £20,000–£30,000 per QALY gained. Cost results are graphically represented in Fig. 3.Fig. 2 Number of injections per year. Figure 2 represents the number of injections estimated for each year in the model, with linear interpolation between the years. Values between the discrete annual time points should be interpreted with caution

Table 3 Base case results for the comparison of faricimab with aflibercept and ranibizumab biosimilar

Base case parameters	Faricimab vs. aflibercept	Faricimab vs. ranibizumab biosimilar	
Aflibercept	Faricimab	Change	% Change	Ranibizumab biosimilar	Faricimab	Change	% Change	
Number of clinic attendances:	55,217	43,766	– 11,451	– 20.74	58,681	43,681	– 15,000	– 25.56	
 Service routine	54,855	43,766	– 11,089	– 20.22	54,860	43,681	– 11,179	– 20.38	
 Service OOH	362	0	– 362	– 100.00	3821	0	– 3821	– 100.00	
Number of injections given	76,771	60,796	– 15,975	– 20.81	81,461	60,637	– 20,824	– 25.56	
Total costs	£84,043,699	£68,935,090	– £15,108,609	– 17.98	£66,697,368	£68,766,457	£2,069,088	3.10	
 Drug costs	£62,645,307	£52,102,172	– £10,543,135	– 16.83	£42,640,750	£51,966,115	£9,325,365	21.87	
 Service costs	£21,398,392	£16,832,918	– £4,565,474	– 21.34	£24,056,618	£16,800,342	– £7,256,276	– 30.16	
 Service–routine	£21,056,057	£16,832,918	– £4,223,139	– 20.06	£21,056,770	£16,800,342	– £4,256,428	– 20.21	
 Service–OOH	£342,335	£0	– £342,335	– 100.00	£2,999,848	£0	– £2,999,848	– 100.00	
Number of delayed visits	12,600	4	– 12,596	– 99.97	18,912	2	– 18,910	– 99.99	
 wAMD	6608	3	– 6605	– 99.96	13,824	1	– 13,823	– 99.99	
 DMO	5993	1	– 5991	– 99.98	5088	1	– 5087	– 99.99	
Change in mean duration of delay, in weeks	2.60	0.12	– 2.48	– 95.28	3.76	0.10	– 3.66	– 97.24	
 wAMD	1.74	0.11	– 1.63	– 93.43	3.74	0.10	– 3.64	– 97.38	
 DMO	3.47	0.10	– 3.37	– 97.22	3.82	0.07	– 3.75	– 98.17	
Direct QALY loss due to delays	– 49.13	0.00	49.13	– 100.00	– 78.65	0.00	78.65	– 100.00	
 wAMD	– 5.93	0.00	5.93	– 100.00	– 38.12	0.00	38.12	– 100.00	
 DMO	– 43.19	0.00	43.19	– 100.00	– 40.53	0.00	40.53	– 100.00	
Loss of potential QALY gain due to delays	– 10.94	0.00	10.94	– 100.00	– 27.06	0.00	27.06	– 100.00	
 wAMD	– 3.06	0.00	3.06	– 100.00	– 19.67	0.00	19.67	– 100.00	
 DMO	– 7.88	0.00	7.88	– 100.00	– 7.39	0.00	7.39	– 100.00	
Total change in QALY, −/+, loss/gain	– 60.06	0.00	60.06	– 100.00	– 105.71	0.00	105.70	– 100.00	
 wAMD	– 8.99	0.00	8.99	– 100.00	– 57.79	0.00	57.79	– 100.00	
 DMO	– 51.07	0.00	51.07	– 100.00	– 47.92	0.00	47.92	– 100.00	
 ICER (£/QALY)		Dominant				19,574			
DMO Diabetic macular oedema, ICER incremental cost-effectiveness ratio, OOH out of hours, QALY quality-adjusted life year, wAMD wet age-related macular degeneration

Fig. 3 Total costs and resource use per year

Scenario Analyses

A summary of results from scenario analyses that tested different model assumptions is presented in Table 4. Overall, results show that faricimab is still dominant compared with aflibercept in all scenarios and is cost-effective compared with ranibizumab in all but two scenarios. Faricimab is most cost-saving and provides the highest NMB when there is no capacity constraint and all patients can be treated in time. The lowest NMB was observed in a scenario where the most conservative treatment interval assumption was applied to faricimab in DMO. In such a scenario, faricimab still reduced almost all treatment delays compared with aflibercept and ranibizumab biosimilar, but the gain in QALYs and savings in OOH appointments were not enough to compensate for the increased drug acquisition cost when compared with ranibizumab biosimilar. Table 4 Scenario analysis results for the comparison of faricimab with aflibercept and ranibizumab biosimilar

Scenario number	Scenario	Faricimab vs	Difference in costs, £	Difference in QALYsa	ICER (£/QALY)	NMB, £	Difference in number of injections, n (%)	Difference in number of attendances, n (%)	Difference in number of delays, n (%)	
	Minimal (without) constraints	Aflibercept	– 24,319,266	0.00	–	24,319,266	– 24,679 (– 28.88)	– 17,386 (– 28.45)	No difference	
Ranibizumab	– 16,189,028	0.00	–	16,189,028	– 45,570 (– 42.87)	– 32,811 (– 42.85)	No difference	
	Base case	Aflibercept	– 15,108,609	60.06	Dominant	16,309,863	– 15,975 (– 20.81)	– 11,451 (– 20.74)	– 12,596 (– 99.97)	
Ranibizumab	2,069,088	105.70	19,574	45,011	– 20,824 (– 25.56)	– 15,000 (– 25.56)	– 18,910 (– 99.99)	
1	Discontinuation: rates based on previous HTA	Aflibercept	– 4,527,333	19.77	Dominant	4,922,728	– 4739 (– 19.34)	– 3398 (– 19.37)	– 2915 (– 99.91)	
Ranibizumab	– 1,294,805	46.01	Dominant	2,214,937	– 9010 (– 31.23)	– 6550 (– 31.61)	– 8657 (– 99.97)	
2	Regimen and treatment interval	Aflibercept	– 10,304,018	78.22	Dominant	11,868,401	– 11,471 (– 14.74)	– 8423 (– 15.07)	– 15,594 (– 99.80)	
Ranibizumab	8,087,560	105.66	76,544	– 5,974,380	– 15,443 (– 18.96)	– 11,335 (– 19.32)	– 18,879 (– 99.83)	
3	Cost multiplier for out-of-hours appointments: 3	Aflibercept	– 15,279,777	60.06	Dominant	16,481,030	– 15,975 (– 20.81)	– 11,451 (– 20.74)	– 12,596 (– 99.97)	
Ranibizumab	569,164	105.70	5384	1,544,935	– 20,824 (– 25.56)	– 15,000 (– 25.56)	– 18,910 (– 99.99)	
4	Utility value for VA per treatment delay: Czoski-Murray et al. [63]	Aflibercept	– 15,108,609	31.88	Dominant	15,746,198	– 15,975 (– 20.81)	– 11,451 (– 20.74)	– 12,596 (– 99.97)	
Ranibizumab	2,069,088	56.10	36,879	– 946,990	– 20,824 (– 25.56)	– 15,000 (– 25.56)	– 18,910 (– 99.99)	
5	Maximum delay time allowed: decrease delay time by 1 week	Aflibercept	– 15,341,990	58.33	Dominant	16,508,509	– 16,199 (– 20.97)	– 11,549 (– 20.89)	– 12,255 (– 99.98)	
Ranibizumab	– 1,552,629	91.54	Dominant	3,383,345	– 24,206 (– 28.50)	– 17,428 (– 28.50)	– 21,238 (– 99.98)	
6	Patient population growth: 5.4%	Aflibercept	– 14,319,547	66.18	Dominant	15,643,190	– 15,249 (– 19.79)	– 10,899 (– 19.69)	– 15,912 (– 99.96)	
Ranibizumab	2,330,694	110.24	21,143	– 125,959	– 20,656 (– 25.06)	– 14,877 (– 25.08)	– 19,412 (– 99.98)	
HTA Health technology assessment, ICER incremental cost-effectiveness ratio; NMB net monetary benefit, QALY quality-adjusted life year; VA visual acuity

aTotal change in QALY (−/ + , loss/gain) between faricimab and aflibercept/ranibizumab biosimilar

Discussion

Interpretation of Results

In this study, we evaluated how capacity constraints affect the cost and effectiveness of pharmacological therapies for the treatment of wAMD and DMO. Economic evaluation methods typically focus on budget constraints, overlooking the quantification of the broader impact of multiple health system capacity limitations on the implementation process [44, 45]. The results of the present study showed that switching to faricimab from aflibercept or ranibizumab biosimilar reduces the number of injections that patients required and therefore reduces the number of delayed appointments. The cost and QALY impact of faricimab were dependent on the assumed constraints present in the system, such as the number of available clinic appointments, allowed treatment delay and increase in demand over time. The results remained consistent even when incorporating demand-side determinants, such as treatment discontinuation.

Model Uncertainty

In the literature, treatments with a lower discontinuation rate result in increased costs but also a gain in terms of QALYs because of prolonged efficacy [46]. In our analysis, we conservatively adopted the same assumption as used in other economic evaluations where the same discontinuation rate is applied for all alternatives and discontinuation would not lead to loss of QALYs for patients [47–49]. The observed outcomes in the scenario with discontinuation are explained by the fact that including it leads to a decrease in overall demand, thereby easing the burden on the healthcare system. Consequently, there is a reduced need for frequent injections for all therapies. This decrease in the frequency of injections also means that the benefit of avoiding treatment delays is smaller, given fewer patients have to wait for an appointment. On the cost side, compared with ranibizumab biosimilar, there are savings because the average number of injections per patient drops more noticeably. However, with aflibercept, as time progresses, the average number of injections between the two treatments becomes more similar, which results in smaller cost savings.

In several prior modelling studies, analyses have focused on the recommended dosage stated on the labels. This may not always align with clinical practice, where physicians individualise treatment intervals based on disease activity [46]; also, baseline characteristics of patients in the real world differ from those of patients in clinical trials. Conversely, some analyses based on claims databases reveal minimal differences between treatment alternatives; for example, a study comparing the use of aflibercept and ranibizumab in wAMD showed no significant variation in the average number of injections administered [50]. Concerning this, some authors note that, in real-world settings, the intervals of administration and average number of injections often vary from those in pivotal studies, partly because of resource constraints and limited capacity to implement complete treatment protocols [46, 50–52].

In this analysis, we incorporated alternative administration frequencies, altering the treatment intervals applied in the base case. For faricimab in DMO and aflibercept in wAMD, the proportions of patients requiring treatment every 4 weeks and every 8 weeks were combined in the base case; all of these patients were assumed to be on an 8-week treatment interval in order to minimise model complexity. Scenario 2 altered this assumption and assumed all patients on 4- or 8-week treatment intervals would be on a 4-week treatment interval. This conservative scenario deliberately increased the clinic capacity used for treating patients with DMO with faricimab and patients with wAMD with aflibercept. Shorter administration intervals could lead to system overload from increased treatment demand, ultimately causing more OOH appointments and more delays in attendances. In this scenario, faricimab was associated with reduced cost savings and NMB compared with aflibercept, but avoided more QALY losses due to delays than estimated in the base case. Compared with ranibizumab biosimilar, using faricimab avoided 105.70 lost QALYs as a result of reducing almost all treatment delays that would have happened if patients were treated with ranibizumab biosimilar; nevertheless, this difference did not compensate for the higher cost of faricimab, leading to a negative net monetary benefit and an ICER above the NICE £20,000 to £30,000 per QALY threshold for cost-effectiveness.

In a scenario with an increase in the annual growth rate in the number of new patients treated from 2.70% to 5.40%, the results did not change substantially from the base case. In the world with faricimab, more QALYs were generated because of a larger number of delays avoided. However, expanding treatment to more patients may incur higher costs. Despite the advantages of interventions that require fewer injections, an overall rise in the number of injections because of an increase in new patients and reduced administration intervals in the induction phase could offset the QALY gains.

Reducing maximum waiting times by 1 week (priority 1: 1 week for wAMD initial visits; priority 2: 3 weeks for wAMD follow-up visits and DMO initial visits; priority 3: 5 weeks for DMO follow-up visits) imposes higher strain on system capacity. In such a case, more patients are treated using OOH capacity because there is a greater emphasis on completing treatment in time. This means more savings associated with using faricimab because this agent reduces the need for OOH appointments, but there is a smaller incremental gain in QALYs for faricimab because there are fewer delays. As a result, the intervention becomes dominant compared with the other two treatments.

The findings suggest that the cost-effectiveness of faricimab increases in the presence of greater system capacity constraints. However, it is important to interpret these results in the context of this study, which is not an exhaustive cost-effectiveness analysis but rather a case study focused on how capacity constraints can alter cost-effectiveness outcomes. Empirical studies have shown that, when capacity constraints are a critical aspect of the evaluated system, incorporating these elements into models considerably affects cost-effectiveness results. This, in turn, can lead to different resource allocation decisions, ultimately influencing clinical practice [53].

Comparison Against Previous Studies

No studies incorporating capacity constraints into the cost-effectiveness analysis of a treatment for wAMD and DMO in ophthalmology were found. However, one previous study assessed the impact of different approaches to organising and delivering glaucoma patient services at a major public hospital [54]. The study’s analysis went beyond treatment pathways to include service delivery aspects such as follow-up times and booking cycles [54].

In other disease areas, studies have integrated factors that have an impact on resource constraints into economic evaluations [55–57]. In oncology, resource constraints have been quantified in various ways—for example, through the capacity to perform biopsy and other testing [58], through drug wastage during the implementation of a new intervention [59], in relation to a changing patient population [60] and in relation to patient adherence [61]. A major difference between these studies and our study is that these previous studies incorporated a consideration for resource constraints as part of a complete cost-effectiveness analysis that explicitly models the clinical effectiveness of health interventions. In contrast, our model assumes that all interventions are equally effective in preserving patients’ vision, and that the only difference in effectiveness is due to delayed injections resulting from resource constraints. Both these earlier studies and our study have shown that the inclusion of constraints in the economic evaluation will modify or affect the cost-effectiveness of a new health technology. Resource constraints that limit implementation can make a new technology less cost-effective, causing interventions that are cost-effective to become wasteful in the short term [62].

Study Limitations

Like all simulation models, this study may not fully represent real-world clinical situations and, therefore, results should be interpreted with caution. This study included several limitations, primarily because of reliance on the quality and certainty of available evidence. Detailed national statistics on capacity constraints in practices administering injections for patients with wAMD and DMO are not available; therefore, certain assumptions had to be made. The capacity constraints incorporated in the model were selected in consultation with a clinical expert, yet there may be other constraints not captured in the model that affect the cost-effectiveness of treatments for wAMD or DMO. The model time horizon was sufficiently long to fully capture all future costs and benefits of the technologies, and it did not require lengthy extrapolation given the potential for future drug cost changes and care process modifications that could alter system constraints and introduce uncertainty. Discontinuation rates were based on those used in previous HTAs [17, 18], but comparability is limited by varying definitions across studies. Although the reduction in QALYs due to early treatment discontinuation was not included, this was unlikely to substantially change the study conclusions given the same assumed discontinuation rate across all treatments. This scenario analysis used an annual discontinuation rate, not accounting for possible differences in administration frequencies, as has been suggested in the literature [63]. Additionally, treatment switching was not modeled because the choice of subsequent therapy remains largely arbitrary owing to limited evidence on treatment sequences, number of injections and subsequent effectiveness. Finally, drug acquisition costs applied in the case study were calculated based on the list price of treatments without considering the true costs for the NHS based on patient access schemes; therefore, the estimated cost savings associated with faricimab may deviate from those occurring in the real-world setting.

Recommendations for Future Research

More information is required to improve the functionality of the model in capturing the supply- and demand-side dynamics of newer treatments with different mechanisms of action, such as faricimab, in the wAMD and DMO population. It is important to be able to calibrate the model as more clinical evidence as well as real-world evidence emerge regarding treatment schedules, the relationship between injection frequency and clinical outcomes and treatment sequences.

Future economic evaluation of treatments for wAMD and DMO should aim to include capacity constraints; their inclusion provides valuable additional information beyond cost-effectiveness. Moreover, for any economic evaluation of new healthcare interventions, it will be pertinent to consider if and how the new technology could affect health system capacity and how this will affect the cost-effectiveness of the new intervention. This information is critical for guiding resource allocation decisions and facilitating implementation strategies among payers and providers.

Conclusion

This study demonstrated that it is crucial to integrate the influence of resource constraints into economic evaluations, especially in disease areas like ophthalmology, in which constrained resources have been shown to reduce the real-world health benefits of treatment. Integrating these constraints into cost-effectiveness modelling has the potential to reshape the decision-making process, providing additional information on the impact of a new technology on the healthcare system’s capacity and offering a more realistic and sustainable basis for decision-making.

The use of faricimab to treat patients addresses resource constraints by reducing injection frequency and minimising delays, with notable benefits in this simulation exercise. These benefits include net cost savings from the healthcare payer perspective and improved QALY outcomes for patients with wAMD and DMO. As shown here, implementing faricimab broadly in clinical practice not only streamlines resource utilisation but also enhances patient outcomes by minimising delays and injections in a resource-constrained health system. Although ranibizumab biosimilar is the least costly option in terms of drug acquisition costs, in the presence of resource constraints, it is associated with considerable QALY losses due to treatment delays; in this case, faricimab is cost-effective compared with ranibizumab biosimilar.

Authorship

All authors made substantial contributions to the work, drafted or revised the manuscript critically for important intellectual content, approved the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy of integrity of any part of the work are appropriately investigated and resolved.

Medical Writing/Editorial Assistance

Editorial support was provided by Karl Read who is an Editorial Consultant at Putnam. Putnam has received funding from F. Hoffmann-La Roche Ltd.

Author Contributions

Tara Li: conceptualisation; methodology; formal analysis; writing—original draft, review, and editing. Vladislav Berdunov: conceptualisation; methodology; writing—original draft, review and editing. Robin Hamilton: conceptualisation; clinical expert input; writing—review and editing. Yaneth Gil Rojas: formal analysis; writing—original draft, review and editing. Christian Bührer: conceptualisation; methodology; writing—review and editing. Oliver Cox: conceptualisation; methodology; writing—review and editing. Roelien Postema: conceptualisation; writing—review and editing. Marloes Bagijn: conceptualisation; methodology; writing—review and editing.

Funding

The study and Rapid Service Fee were funded by F. Hoffmann-La Roche Ltd, which is the manufacturer of faricimab.

Data Availability

The parameter inputs used in the model were identified from sources in open domain. All parameters are reported in the article.

Declarations

Conflict of Interest

Tara Li, Vladislav Berdunov, Yaneth Gil Rojas, and Roelien Postema are employees of Putnam, which has received funding from F. Hoffmann-La Roche Ltd. Christian Bührer, Oliver Cox, and Marloes Bagijn are employees of F. Hoffmann-La Roche Ltd. Robin Hamilton has received financial support for consultancy from F. Hoffmann-La Roche Ltd, travel and speaker honoraria and being on advisory boards for F. Hoffmann-La Roche Ltd, Bayer, Teva, and Biogen, and research grants from F. Hoffmann-La Roche Ltd, Bayer, Perspectum, and Quantel.

Ethical Approval

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Prior presentation: The manuscript is based on work that has been previously presented at a poster session of the ISPOR Europe 2023 Conference (Copenhagen): https://doi.org/10.1016/j.jval.2023.09.673.
==== Refs
References

1. Thokala P Dixon S Jahn B Resource modelling: the missing piece of the HTA jigsaw? Pharmacoeconomics 2015 33 3 193 203 10.1007/s40273-014-0228-9 25411095
Thokala P, Dixon S, Jahn B. Resource modelling: the missing piece of the HTA jigsaw? Pharmacoeconomics. 2015;33(3):193–203.25411095
2. Gale R Cox O Keenan C Chakravarthy U Health technology assessment of new retinal treatments; the need to capture healthcare capacity issues Eye 2022 36 12 2236 2238 10.1038/s41433-022-02149-5 35764875
Gale R, Cox O, Keenan C, Chakravarthy U. Health technology assessment of new retinal treatments; the need to capture healthcare capacity issues. Eye. 2022;36(12):2236–8.35764875
3. NHS England. Consultant-led referral to treatment waiting times data 2022–23. 2023. https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/rtt-data-2022-23/. Accessed 22 July 2024.
4. Royal College of Ophthalmologists. Public Accounts Committee inquiry—managing NHS backlogs & waiting times: Royal College of Ophthalmologists response 2022. https://www.rcophth.ac.uk/wp-content/uploads/2022/11/RCOphth-response-to-Public-Accounts-Committee-inquiry-Managing-NHS-backlogs-and-waiting-times-Nov-2022.pdf. Accessed 22 July 2024.
5. Royal College of Ophthalmologists. Workforce Census 2018. https://www.rcophth.ac.uk/wp-content/uploads/2020/05/RCOphth-Workforce-Census-2018.pdf. Accessed 22 July 2024.
6. Association of Optometrists. NHS backlog emergency forcing people to go private or risk permanent sight loss, latest survey finds. 2023. https://www.aop.org.uk/our-voice/media-centre/press-releases/2023/11/23/nhs-backlog-emergency-forcing-people-to-go-private-or-risk-permanent-sight-loss-latest-survey-finds. Accessed 22 July 2024.
7. National Institute for Health and Care Excellence. Clinical Knowledge Summaries (CKS): macular degeneration—age-related. 2022. https://cks.nice.org.uk/topics/macular-degeneration-age-related/. Accessed 22 July 2024.
8. National Eye Institute. Diabetic Retinopathy. 2024. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/diabetic-retinopathy. Accessed 22 July 2024.
9. Holman N Forouhi N Goyder E Wild S The Association of Public Health Observatories (APHO) diabetes prevalence model: estimates of total diabetes prevalence for England, 2010–2030 Diabet Med 2011 28 5 575 582 10.1111/j.1464-5491.2010.03216.x 21480968
Holman N, Forouhi N, Goyder E, Wild S. The Association of Public Health Observatories (APHO) diabetes prevalence model: estimates of total diabetes prevalence for England, 2010–2030. Diabet Med. 2011;28(5):575–82.21480968
10. Li JQ Welchowski T Schmid M Mauschitz MM Holz FG Finger RP Prevalence and incidence of age-related macular degeneration in Europe: a systematic review and meta-analysis Br J Ophthalmol 2020 104 8 1077 1084 10.1136/bjophthalmol-2019-314422 31712255
Li JQ, Welchowski T, Schmid M, Mauschitz MM, Holz FG, Finger RP. Prevalence and incidence of age-related macular degeneration in Europe: a systematic review and meta-analysis. Br J Ophthalmol. 2020;104(8):1077–84.31712255
11. National Institute for Health and Care Excellence. Age-related macular degeneration [NG82]. 2018. https://www.nice.org.uk/guidance/ng82. Accessed 22 July 2024.
12. National Institute for Health and Care Excellence. Faricimab for treating diabetic macula oedema [TA799]. 2022. https://www.nice.org.uk/guidance/ta799. Accessed 22 July 2024.
13. Chopra R Preston GC Keenan TDL Intravitreal injections: past trends and future projections within a UK tertiary hospital Eye (Lond) 2022 36 7 1373 1378 10.1038/s41433-021-01646-3 34172943
Chopra R, Preston GC, Keenan TDL, et al. Intravitreal injections: past trends and future projections within a UK tertiary hospital. Eye (Lond). 2022;36(7):1373–8.34172943
14. Holz FG Tadayoni R Beatty S Multi-country real-life experience of anti-vascular endothelial growth factor therapy for wet age-related macular degeneration Br J Ophthalmol 2015 99 2 220 226 10.1136/bjophthalmol-2014-305327 25193672
Holz FG, Tadayoni R, Beatty S, et al. Multi-country real-life experience of anti-vascular endothelial growth factor therapy for wet age-related macular degeneration. Br J Ophthalmol. 2015;99(2):220–6.25193672
15. National Institute for Health and Care Excellence. Ranibizumab and pegaptanib for the treatment of age-related macular degeneration [TA155]. 2008. https://www.nice.org.uk/guidance/ta155. Accessed 22 July 2024.
16. National Institute for Health and Care Excellence. Ranibizumab for treating diabetic macular oedema [TA274]. 2013. https://www.nice.org.uk/guidance/ta274. Accessed 22 July 2024.
17. National Institute for Health and Care Excellence. Aflibercept solution for injection for treating wet age-related macular degeneration. 2013. https://www.nice.org.uk/guidance/ta294. Accessed 22 July 2024.
18. National Institute for Health and Care Excellence. Aflibercept for treating diabetic macular oedema [TA346]. 2015. https://www.nice.org.uk/guidance/ta346. Accessed 22 July 2024.
19. National Institute for Health and Care Excellence. Brolucizumab for treating wet age-related macular degeneration [TA672]. 2021. https://www.nice.org.uk/guidance/ta672. Accessed 22 July 2024.
20. National Institute for Health and Care Excellence. Brolucizumab for treating diabetic macular oedema [TA820]. 2022. https://www.nice.org.uk/guidance/ta820. Accessed 22 July 2024.
21. National Institute for Health and Care Excellence. Faricimab for treating wet age-related macular degeneration [TA800]. 2022. https://www.nice.org.uk/guidance/ta800. Accessed 22 July 2024.
22. Lim JH Wickremasinghe SS Xie J Delay to treatment and visual outcomes in patients treated with anti-vascular endothelial growth factor for age-related macular degeneration Am J Ophthalmol 2012 153 4 678 86.e2 10.1016/j.ajo.2011.09.013 22245460
Lim JH, Wickremasinghe SS, Xie J, et al. Delay to treatment and visual outcomes in patients treated with anti-vascular endothelial growth factor for age-related macular degeneration. Am J Ophthalmol. 2012;153(4):678-86.e2.22245460
23. Im JH Jin Y-P Chow R Dharia RS Yan P Delayed anti-VEGF injections during the COVID-19 pandemic and changes in visual acuity in patients with three common retinal diseases: a systematic review and meta-analysis Surv Ophthalmol 2022 10.1016/j.survophthal.2022.08.002 35970234
Im JH, Jin Y-P, Chow R, Dharia RS, Yan P. Delayed anti-VEGF injections during the COVID-19 pandemic and changes in visual acuity in patients with three common retinal diseases: a systematic review and meta-analysis. Surv Ophthalmol. 2022. 10.1016/j.survophthal.2022.08.002.35970234
24. Penha FM Masud M Khanani ZA Thomas M Fong RD Smith K Review of real-world evidence of dual inhibition of VEGF-A and ANG-2 with faricimab in NAMD and DME Int J Retina Vitreous 2024 10 1 5 10.1186/s40942-024-00525-9 38233896
Penha FM, Masud M, Khanani ZA, Thomas M, Fong RD, Smith K, et al. Review of real-world evidence of dual inhibition of VEGF-A and ANG-2 with faricimab in NAMD and DME. Int J Retina Vitreous. 2024;10(1):5.38233896
25. Narayan A, Watson S. Faricimab: the solution to treatment resistant neovascular age-related macular degeneration? A real-world study. Royal College of Ophthalmologists Annual Congress, 25–28 May 2023; Birmingham3.
26. National Institute for Health and Care Excellence. Supporting the health and care system in improving productivity. 2023. https://www.nice.org.uk/productivity. Accessed 22 July 2024.
27. Haider S Thayakaran R Subramanian A Disease burden of diabetes, diabetic retinopathy and their future projections in the UK: cross-sectional analyses of a primary care database BMJ Open 2021 11 7 e050058 10.1136/bmjopen-2021-050058 34253675
Haider S, Thayakaran R, Subramanian A, et al. Disease burden of diabetes, diabetic retinopathy and their future projections in the UK: cross-sectional analyses of a primary care database. BMJ Open. 2021;11(7):e050058.34253675
28. Owen CG Jarrar Z Wormald R Cook DG Fletcher AE Rudnicka AR The estimated prevalence and incidence of late stage age related macular degeneration in the UK Br J Ophthalmol 2012 96 5 752 756 10.1136/bjophthalmol-2011-301109 22329913
Owen CG, Jarrar Z, Wormald R, Cook DG, Fletcher AE, Rudnicka AR. The estimated prevalence and incidence of late stage age related macular degeneration in the UK. Br J Ophthalmol. 2012;96(5):752–6.22329913
29. Giocanti-Auregan A Tadayoni R Grenet T Estimation of the need for bilateral intravitreal anti-VEGF injections in clinical practice BMC Ophthalmol 2016 16 142 10.1186/s12886-016-0317-y 27507298
Giocanti-Auregan A, Tadayoni R, Grenet T, et al. Estimation of the need for bilateral intravitreal anti-VEGF injections in clinical practice. BMC Ophthalmol. 2016;16:142.27507298
30. Garweg JG Štefanickova J Hoyng C Dosing regimens of intravitreal aflibercept for diabetic macular edema beyond the first year: VIOLET, a prospective randomized trial Adv Ther 2022 39 6 2701 2716 10.1007/s12325-022-02119-z 35412227
Garweg JG, Štefanickova J, Hoyng C, et al. Dosing regimens of intravitreal aflibercept for diabetic macular edema beyond the first year: VIOLET, a prospective randomized trial. Adv Ther. 2022;39(6):2701–16.35412227
31. Heier JS Khanani AM Quezada Ruiz C Efficacy, durability, and safety of intravitreal faricimab up to every 16 weeks for neovascular age-related macular degeneration (TENAYA and LUCERNE): two randomised, double-masked, phase 3, non-inferiority trials Lancet 2022 399 10326 729 740 10.1016/S0140-6736(22)00010-1 35085502
Heier JS, Khanani AM, Quezada Ruiz C, et al. Efficacy, durability, and safety of intravitreal faricimab up to every 16 weeks for neovascular age-related macular degeneration (TENAYA and LUCERNE): two randomised, double-masked, phase 3, non-inferiority trials. Lancet. 2022;399(10326):729–40.35085502
32. Hirano T Toriyama Y Takamura Y Outcomes of a 2-year treat-and-extend regimen with aflibercept for diabetic macular edema Sci Rep 2021 11 1 4488 10.1038/s41598-021-83811-y 33627712
Hirano T, Toriyama Y, Takamura Y, et al. Outcomes of a 2-year treat-and-extend regimen with aflibercept for diabetic macular edema. Sci Rep. 2021;11(1):4488.33627712
33. Kertes PJ Galic IJ Greve M Efficacy of a treat-and-extend regimen with ranibizumab in patients with neovascular age-related macular disease: a randomized clinical trial JAMA Ophthalmol 2020 138 3 244 250 10.1001/jamaophthalmol.2019.5540 31917441
Kertes PJ, Galic IJ, Greve M, et al. Efficacy of a treat-and-extend regimen with ranibizumab in patients with neovascular age-related macular disease: a randomized clinical trial. JAMA Ophthalmol. 2020;138(3):244–50.31917441
34. Payne JF Wykoff CC Clark WL Long-term outcomes of treat-and-extend ranibizumab with and without navigated laser for diabetic macular oedema: TREX-DME 3-year results Br J Ophthalmol 2021 105 2 253 257 10.1136/bjophthalmol-2020-316176 32303499
Payne JF, Wykoff CC, Clark WL, et al. Long-term outcomes of treat-and-extend ranibizumab with and without navigated laser for diabetic macular oedema: TREX-DME 3-year results. Br J Ophthalmol. 2021;105(2):253–7.32303499
35. Souied EH Holz FG Hykin PG Efficacy of intravitreal aflibercept treat-and-extend regimen over 2 years for neovascular age-related macular degeneration: ARIES study Invest Ophthalmol Visual Sci 2020 61 7 4210
Souied EH, Holz FG, Hykin PG, et al. Efficacy of intravitreal aflibercept treat-and-extend regimen over 2 years for neovascular age-related macular degeneration: ARIES study. Invest Ophthalmol Visual Sci. 2020;61(7):4210.
36. Wykoff CC Abreu F Adamis AP Efficacy, durability, and safety of intravitreal faricimab with extended dosing up to every 16 weeks in patients with diabetic macular oedema (YOSEMITE and RHINE): two randomised, double-masked, phase 3 trials The Lancet 2022 399 10326 741 755 10.1016/S0140-6736(22)00018-6
Wykoff CC, Abreu F, Adamis AP, et al. Efficacy, durability, and safety of intravitreal faricimab with extended dosing up to every 16 weeks in patients with diabetic macular oedema (YOSEMITE and RHINE): two randomised, double-masked, phase 3 trials. Lancet. 2022;399(10326):741–55.
37. MacEwen CD, Alison; Chang, Lydia. Ophthalmology: GIRFT Programme National Specialty Report. 2019. https://gettingitrightfirsttime.co.uk/wp-content/uploads/2019/12/OphthalmologyReportGIRFT19P-FINAL.pdf. Accessed 22/07/2024
38. Royal College of Ophthalmologists. Commissioning Guidance: Age Related Macular Degeneration Services. 2024. https://www.rcophth.ac.uk/resources-listing/commissioning-guidance-age-related-macular-degeneration-services/. Accessed 22/07/2024
39. NHS England. 2021/22 National Cost Collection Data Publication 2023. Available from: https://www.england.nhs.uk/publication/2021-22-national-cost-collection-data-publication/. Accessed 22/07/2024
40. National Institute for Health and Care Excellence. British National Formulary. 2024. https://bnf.nice.org.uk/. Accessed 22/07/2024
41. Loewenstein A Berger A Daly A Save our Sight (SOS): a collective call-to-action for enhanced retinal care across health systems in high income countries Eye 2023 10.1038/s41433-023-02540-w 37925561
Loewenstein A, Berger A, Daly A, et al. Save our Sight (SOS): a collective call-to-action for enhanced retinal care across health systems in high income countries. Eye. 2023. 10.1038/s41433-023-02540-w.37925561
42. Hodgson R Reason T Trueman D Challenges associated with estimating utility in wet age-related macular degeneration: a novel regression analysis to capture the bilateral nature of the disease Adv Ther 2017 34 10 2360 2370 10.1007/s12325-017-0620-x 29019023
Hodgson R, Reason T, Trueman D, et al. Challenges associated with estimating utility in wet age-related macular degeneration: a novel regression analysis to capture the bilateral nature of the disease. Adv Ther. 2017;34(10):2360–70.29019023
43. Czoski-Murray C Carlton J Brazier J Young T Papo NL Kang HK Valuing condition-specific health states using simulation contact lenses value in health Value Health 2019 12 5 793 799 10.1111/j.1524-4733.2009.00527.x
Czoski-Murray C, Carlton J, Brazier J, Young T, Papo NL, Kang HK. Valuing condition-specific health states using simulation contact lenses value in health. Value Health. 2009;12(5):793–99. 10.1111/j.1524-4733.2009.00527.x
44. van Baal P Morton A Severens JL Health care input constraints and cost effectiveness analysis decision rules Soc Sci Med 2018 200 59 64 10.1016/j.socscimed.2018.01.026 29421472
van Baal P, Morton A, Severens JL. Health care input constraints and cost effectiveness analysis decision rules. Soc Sci Med. 2018;200:59–64.29421472
45. Brennan A Chick SE Davies R A taxonomy of model structures for economic evaluation of health technologies Health Econ 2006 15 12 1295 1310 10.1002/hec.1148 16941543
Brennan A, Chick SE, Davies R. A taxonomy of model structures for economic evaluation of health technologies. Health Econ. 2006;15(12):1295–310.16941543
46. Ferrante N Ritrovato D Bitonti R Furneri G Cost-effectiveness analysis of brolucizumab versus aflibercept for the treatment of neovascular age-related macular degeneration (nAMD) in Italy BMC Health Serv Res 2022 22 1 573 10.1186/s12913-022-07972-w 35484540
Ferrante N, Ritrovato D, Bitonti R, Furneri G. Cost-effectiveness analysis of brolucizumab versus aflibercept for the treatment of neovascular age-related macular degeneration (nAMD) in Italy. BMC Health Serv Res. 2022;22(1):573.35484540
47. Ming J Zhang Y Xu X Cost–effectiveness analysis of intravitreal aflibercept in the treatment of diabetic macular edema in China J Compar Eff Res 2019 9 3 161 175 10.2217/cer-2019-0174
Ming J, Zhang Y, Xu X, et al. Cost–effectiveness analysis of intravitreal aflibercept in the treatment of diabetic macular edema in China. J Compar Eff Res. 2019;9(3):161–75.
48. Yanagi Y Takahashi K Iida T Cost-effectiveness analysis of ranibizumab biosimilar for neovascular age-related macular degeneration in Japan Ophthalmol Ther 2023 12 4 2005 2021 10.1007/s40123-023-00715-y 37171557
Yanagi Y, Takahashi K, Iida T, et al. Cost-effectiveness analysis of ranibizumab biosimilar for neovascular age-related macular degeneration in Japan. Ophthalmol Ther. 2023;12(4):2005–21.37171557
49. Yu JS Carlton R Agashivala N Hassan T Wykoff CC Brolucizumab vs aflibercept and ranibizumab for neovascular age-related macular degeneration: a cost-effectiveness analysis J Manag Care Spec Pharm 2021 27 6 743 752 34057392
Yu JS, Carlton R, Agashivala N, Hassan T, Wykoff CC. Brolucizumab vs aflibercept and ranibizumab for neovascular age-related macular degeneration: a cost-effectiveness analysis. J Manag Care Spec Pharm. 2021;27(6):743–52.34057392
50. Kemp-Casey A Pratt N Ramsay E Roughead EE Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use Appl Health Econ Health Policy 2019 17 411 417 10.1007/s40258-018-0440-4 30362070
Kemp-Casey A, Pratt N, Ramsay E, Roughead EE. Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use. Appl Health Econ Health Policy. 2019;17:411–7.30362070
51. Carrasco J Pietsch GA Nicolas MP Koerber C Bennison C Yoon J Real-world effectiveness and real-world cost-effectiveness of intravitreal aflibercept and intravitreal ranibizumab in neovascular age-related macular degeneration: systematic review and meta-analysis of real-world studies Adv Ther 2020 37 1 300 315 10.1007/s12325-019-01147-6 31728825
Carrasco J, Pietsch GA, Nicolas MP, Koerber C, Bennison C, Yoon J. Real-world effectiveness and real-world cost-effectiveness of intravitreal aflibercept and intravitreal ranibizumab in neovascular age-related macular degeneration: systematic review and meta-analysis of real-world studies. Adv Ther. 2020;37(1):300–15.31728825
52. Maniadakis N Konstantakopoulou E Cost effectiveness of treatments for diabetic retinopathy: a systematic literature review Pharmacoeconomics 2019 37 8 995 1010 10.1007/s40273-019-00800-w 31012025
Maniadakis N, Konstantakopoulou E. Cost effectiveness of treatments for diabetic retinopathy: a systematic literature review. Pharmacoeconomics. 2019;37(8):995–1010.31012025
53. Standfield LB Comans TA Scuffham PA An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting Eur J Health Econ 2017 18 1 33 47 10.1007/s10198-015-0756-z 26715578
Standfield LB, Comans TA, Scuffham PA. An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting. Eur J Health Econ. 2017;18(1):33–47.26715578
54. Crane GJ Kymes SM Hiller JE Casson R Martin A Karnon JD Accounting for costs, QALYs, and capacity constraints: using discrete-event simulation to evaluate alternative service delivery and organizational scenarios for hospital-based glaucoma services Med Decis Making 2013 33 8 986 997 10.1177/0272989X13478195 23515216
Crane GJ, Kymes SM, Hiller JE, Casson R, Martin A, Karnon JD. Accounting for costs, QALYs, and capacity constraints: using discrete-event simulation to evaluate alternative service delivery and organizational scenarios for hospital-based glaucoma services. Med Decis Making. 2013;33(8):986–97.23515216
55. Wright SJ Newman WG Payne K Quantifying the impact of capacity constraints in economic evaluations: an application in precision medicine Med Decis Making 2022 42 4 538 553 10.1177/0272989X211053792 34694170
Wright SJ, Newman WG, Payne K. Quantifying the impact of capacity constraints in economic evaluations: an application in precision medicine. Med Decis Making. 2022;42(4):538–53.34694170
56. Hontelez JA Chang AY Ogbuoji O de Vlas SJ Barnighausen T Atun R Changing HIV treatment eligibility under health system constraints in sub-Saharan Africa: investment needs, population health gains, and cost-effectiveness AIDS 2016 30 15 2341 2350 10.1097/QAD.0000000000001190 27367487
Hontelez JA, Chang AY, Ogbuoji O, de Vlas SJ, Barnighausen T, Atun R. Changing HIV treatment eligibility under health system constraints in sub-Saharan Africa: investment needs, population health gains, and cost-effectiveness. AIDS. 2016;30(15):2341–50.27367487
57. Jahn B Pfeiffer KP Theurl E Tarride JE Goeree R Capacity constraints and cost-effectiveness: a discrete event simulation for drug-eluting stents Med Decis Making 2010 30 1 16 28 10.1177/0272989X09336075 19789389
Jahn B, Pfeiffer KP, Theurl E, Tarride JE, Goeree R. Capacity constraints and cost-effectiveness: a discrete event simulation for drug-eluting stents. Med Decis Making. 2010;30(1):16–28.19789389
58. Vanderlaan BF Broder MS Chang EY Oratz R Bentley TG Cost-effectiveness of 21-gene assay in node-positive, early-stage breast cancer Am J Manag Care 2011 17 7 455 464 21819166
Vanderlaan BF, Broder MS, Chang EY, Oratz R, Bentley TG. Cost-effectiveness of 21-gene assay in node-positive, early-stage breast cancer. Am J Manag Care. 2011;17(7):455–64.21819166
59. Delea TE Amdahl J Chit A Amonkar MM Cost-effectiveness of lapatinib plus letrozole in her2-positive, hormone receptor-positive metastatic breast cancer in Canada Curr Oncol 2013 20 5 e371 e387 10.3747/co.20.1394 24155635
Delea TE, Amdahl J, Chit A, Amonkar MM. Cost-effectiveness of lapatinib plus letrozole in her2-positive, hormone receptor-positive metastatic breast cancer in Canada. Curr Oncol. 2013;20(5):e371–87.24155635
60. Garrison LP Jr Veenstra DL The economic value of innovative treatments over the product life cycle: the case of targeted trastuzumab therapy for breast cancer Value Health 2009 12 8 1118 1123 10.1111/j.1524-4733.2009.00572.x 19624617
Garrison LP Jr, Veenstra DL. The economic value of innovative treatments over the product life cycle: the case of targeted trastuzumab therapy for breast cancer. Value Health. 2009;12(8):1118–23.19624617
61. McCowan C Wang S Thompson AM Makubate B Petrie DJ The value of high adherence to tamoxifen in women with breast cancer: a community-based cohort study Br J Cancer 2013 109 5 1172 1180 10.1038/bjc.2013.464 23949153
McCowan C, Wang S, Thompson AM, Makubate B, Petrie DJ. The value of high adherence to tamoxifen in women with breast cancer: a community-based cohort study. Br J Cancer. 2013;109(5):1172–80.23949153
62. Wright SJ Newman WG Payne K Accounting for capacity constraints in economic evaluations of precision medicine: a systematic review Pharmacoeconomics 2019 37 8 1011 1027 10.1007/s40273-019-00801-9 31087278
Wright SJ, Newman WG, Payne K. Accounting for capacity constraints in economic evaluations of precision medicine: a systematic review. Pharmacoeconomics. 2019;37(8):1011–27.31087278
63. Bakri SJ Karcher H Andersen S Souied EH Anti–vascular endothelial growth factor treatment discontinuation and interval in neovascular age-related macular degeneration in the United States Am J Ophthalmol 2022 242 189 196 10.1016/j.ajo.2022.06.005 35738393
Bakri SJ, Karcher H, Andersen S, Souied EH. Anti–vascular endothelial growth factor treatment discontinuation and interval in neovascular age-related macular degeneration in the United States. Am J Ophthalmol. 2022;242:189–96.35738393
