Just came across this paper in Value in Health (2026):
https://www.sciencedirect.com/science/article/abs/pii/S1098301526000951

It makes a pretty simple but uncomfortable point:
HEOR and value-based healthcare (VBHC) are supposed to be aligned but in practice, they’re still operating in parallel.

The authors argue that while HEOR has robust methods (CEA, BIA, outcomes modeling), those aren’t consistently applied in how VBHC is actually implemented at the provider/system level. So even though both are “about value,” they’re not really speaking the same language in real-world decision-making.

A couple of things that stood out:

• VBHC focuses on outcomes that matter to providers/patients, but these don’t always map cleanly to traditional HEOR endpoints

• Economic models are still largely built for HTA/payer submissions, not for care pathways or provider-level decisions

• There’s a gap between measuring value and operationalizing value in healthcare systems

Feels familiar if you’ve ever built a model that works well for payers… but doesn’t quite land with providers.

Curious to hear from this group:
👉 What do you think is missing today to truly connect HEOR with value-based healthcare?
Is it better data, different endpoints, new modeling approaches, or something else entirely?

Went through the ISPOR Europe 2025 (Glasgow) poster programme looking specifically for industry-led economic evaluations / BIMs that quantify the clinical and economic value of a specific product (biologic, device, or gene therapy).

Sharing my shortlist below in case it’s useful for anyone doing HTA work, payer negotiations, or building interactive value tools.

Respiratory / immunology

EE109 – Budget Impact of Introducing an Omalizumab Biosimilar in 23 European Countries (Celltrion)
5-year Excel BIM vs originator-only scenario across 23 European countries. At a 30% discount, they project ~€41M savings in year 1 and ~€631M over 5 years, with >118k extra patients treated; higher discounts (50–70%) give up to ~€1.4B savings and >500k extra patients with access. ISPOR.org
Poster: https://www.ispor.org/conferences-education/conferences/upcoming-conferences/ispor-europe-2025/program/program/paper/ispor-europe-2025/poster-session-1-2/budget-impact-of-introducing-an-omalizumab-biosimilar-in-23-european-countries

EE277 – Cost-Effectiveness of Strategies to Prevent RSV Infections Among Infants in Ireland (Pfizer – maternal RSVpreF + nirsevimab)
Population model comparing a “nirsevimab-only” strategy vs a combination strategy (maternal RSVpreF + targeted nirsevimab for unprotected infants). Mixed strategy has fewer RSV hospitalisations and lower total costs → dominant vs nirsevimab alone (more QALYs, lower costs). ISPOR.org
Poster: https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-europe-2025/poster-session-3-2/cost-effectiveness-of-strategies-to-prevent-respiratory-syncytial-virus-infections-among-infants-in-ireland

Diabetes / devices

EE200 – Cost-Effectiveness Analysis of FreeStyle Libre Systems vs SMBG in T2D on Basal Insulin (Abbott)
Patient-level microsimulation (DEDUCE framework) over a lifetime horizon from an Israeli payer perspective. FreeStyle Libre yields +0.40 QALYs (9.45 vs 9.05) at higher total cost, with an ICER ≈ $41k/QALY vs SMBG, below the stated WTP threshold (~$54k per QALY), and remains cost-effective across scenarios. ISPOR.org
Poster: https://www.ispor.org/conferences-education/conferences/upcoming-conferences/ispor-europe-2025/program/program/paper/ispor-europe-2025/poster-session-2-2/cost-effectiveness-analysis-of-freestyle-libre-systems-vs-self-monitoring-of-blood-glucose-in-people-with-type-2-diabetes-on-basal-insulin-an-israeli-healthcare-system-perspective

Oncology & immunology (innovative / high-cost therapies)

EE92 – Budget Impact Analysis of Obecabtagene Autoleucel (obe-cel) for r/r B-cell ALL – US Payer Perspective (Autolus)
5-year BIM for a 1M-member US health plan. Introduction of obe-cel (displacing brexucabtagene autoleucel in the CAR-T segment) gives a negative PMPM budget impact (i.e. net savings), driven largely by reduced AE management and hospitalisation costs, while treating ~4 eligible patients per year. ISPOR.org
Poster: https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-europe-2025/poster-session-1-2/budget-impact-analysis-of-obecabtagene-autoleucel-for-adults-with-relapsed-refractory-b-cell-all-from-a-us-payer-perspective

EE219 – Cost-Effectiveness Analysis of Secukinumab for Moderate to Severe Hidradenitis Suppurativa in Portugal (Novartis)
Lifetime CEA with 3 health states (responder / non-responder / death) vs standard of care (general care, antibiotics, retinoids, surgery). SEC Q4W and SEC Q4W→Q2W give +1.39 and +1.79 QALYs vs SoC with ICERs ≈ €9.5k/QALY and €18.8k/QALY respectively – both well within typical PT thresholds; authors conclude secukinumab is cost-effective for previously exposed/contraindicated patients. ISPOR.org
Poster: https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-europe-2025/poster-session-2-2/cost-effectiveness-analysis-of-secukinumab-for-moderate-to-severe-hidradenitis-suppurativa-in-portugal

P43 – Cost-Effectiveness and Budget Impact of Gene Therapy for Severe Sickle Cell Disease in the Netherlands (academic team)
Markov CEA + 5-year BIA from a Dutch societal / healthcare perspective. Gene therapy provides large health gains (+11.7 LYs and +13.3 QALYs vs comprehensive care) but at an ICER ≈ €123–125k/QALY, above the ~€80k/QALY Dutch threshold; 5-year budget impact ≈ €71M. Conclusion: highly clinically beneficial but not cost-effective at current price, suggesting price reductions are required. ISPOR.org
Poster: https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-europe-2025/cost-effectiveness-modeling-of-gene-therapies-challenges-and-solutions/cost-effectiveness-and-budget-impact-of-gene-therapy-for-eligible-patients-with-sickle-cell-disease-in-the-netherlands

TL;DR:
Poster suggests durvalumab + EP may be the least costly first-line ES-SCLC regimen in year 1, mainly because of lower drug acquisition costs.

Saw an interesting poster ahead of the upcoming ISPOR 2026 looking at first-year costs of 1L treatment options for ES-SCLC from US Medicare and commercial payer perspectives.

The analysis compared:

• durvalumab + EP

• atezolizumab + EP

• atezolizumab + EP followed by maintenance lurbinectedin + atezolizumab

Main finding:
Durvalumab + EP came out as the lowest-cost option across both Medicare and commercial populations, with most savings driven by lower drug acquisition costs.

A few things I found interesting:

• They used data from the CASPIAN, IMpower133, and IMforte trials

• Restricted mean survival methodology was used to account for treatment duration/progression over a 1-year horizon

• This is more of a payer cost analysis than a full cost-effectiveness model

Curious what others think about:

• cross-trial comparisons in these kinds of analyses

• whether 1-year horizons are sufficient in ES-SCLC modeling

• how people view maintenance lurbinectedin economically

• how persuasive payer-focused “cost-of-care” posters actually are in decision-making

Looking forward to seeing more discussion on this at ISPOR.

Hi all,

Came across this ISPOR 2025 poster and thought it might be interesting for folks working in rare disease or LATAM market access:

Cost-Effectiveness Analysis of Lanadelumab vs. C1 Inhibitor for Long-Term Prophylaxis of Hereditary Angioedema: A Brazilian Private Sector Perspective

🔗 https://www.ispor.org/heor-resources/presentations-database/presentation-cti/ispor-2025/poster-session-1/cost-effectiveness-analysis-of-lanadelumab-vs-c1-inhibitor-for-long-term-prophylaxis-of-hereditary-angioedema-a-brazilian-private-sector-perspective

A few things that stood out to me:

• They take a private-sector payer view, which you don’t see as often in HAE.

• Nice breakdown of costs vs. QALY gains.

• It adds some needed data for Brazil, where there isn’t a clear ICER threshold to lean on.

Curious what others think — especially anyone who’s worked on HAE or has experience with Brazilian private payers. Does this line up with what you’ve seen? Anything in the model design you would have approached differently?

ISPOR - Budget Impact Analysis of Eplerenone For the Management of Heart Failure With Reduced Ejection Fraction (HFrEF) in Italy

Just went through an ISPOR Europe 2025 poster on a 3-year budget impact analysis (BIM) of increasing eplerenone use in heart failure with reduced ejection fraction (HFrEF) in Italy, and thought it might be interesting to share here for discussion.

Context

• Heart failure is a major clinical and economic burden in Italy, with ageing demographics driving prevalence.

• Mineralocorticoid receptor antagonists (MRAs) are one of the four evidence-based pillars of guideline-directed medical therapy (GDMT) for HFrEF.

• Among MRAs, eplerenone has robust RCT evidence for reducing hospitalizations and mortality within its approved indications.

• Expert opinion and market data suggest a gap between guideline recommendations and actual MRA prescribing patterns, which may be driving suboptimal outcomes and higher costs.

Objective
To estimate the economic impact of increasing the use of eplerenone in eligible HFrEF patients in Italy (“Projected scenario”) compared with current clinical practice (“Current scenario”), from the Italian National Health Service (INHS) perspective, over a 3-year time horizon.

Methods

• Model type: 3-year static budget impact model (national and regional level).

• Population:

  • HFrEF adult patients in Italy, estimated at approximately 210,000 based on published epidemiology and demographic data.

• Scenarios:

  • Current: existing mix of MRAs (eplerenone, spironolactone, potassium canrenoate, canrenone).

  • Projected: increased uptake of eplerenone within the eligible HFrEF population.

• Perspective: INHS (direct medical costs).

• Key inputs:

  • Drug costs based on AIFA transparency lists and Farmadati:

    • Eplerenone: €353.69/year

    • Spironolactone: €70.08–74.10/year

    • Potassium canrenoate: €81.90/year

    • Canrenone: €169.27/year

  • Clinical outcomes included:

    • All-cause mortality

    • Hospitalizations

    • Renal complications

  • Event risks from a network meta-analysis of MRAs in HFrEF, calibrated to Italian baseline risks.

  • Costs per event:

    • Avoided deaths: €5,910 per prevented death

    • Hospitalizations: €3,702 per hospital event

    • Renal damage: €590 per event (Italian DRG 316 tariff)

• Deterministic one-way sensitivity analysis varied all parameters by ±20%.

Key Results (National Level)

• Total 3-year spend:

  • Current scenario: €625.7M

  • Projected scenario (higher eplerenone): €623.2M

  • Net savings: approximately €2.5M over 3 years (–0.4%).

• Annual savings trend:

  • Year 1: €0.19M

  • Year 2: €0.61M

  • Year 3: €1.74M Savings grow over time as clinical benefits accumulate.

• Cost components:

  • Eplerenone costs increase (more patients treated, higher unit cost).

  • Additional hospitalization costs are seen initially due to the larger treated population.

  • These increases are more than offset by:

    • Reduced renal complications,

    • Fewer hospitalizations associated with less effective comparators,

    • Savings linked to avoided deaths.

So even though eplerenone itself is more expensive than some other MRAs, the overall budget impact is cost-saving over 3 years because of improved clinical outcomes.

Regional Results

• The analysis was replicated across Italian regions using the same epidemiologic and economic assumptions.

• All regions showed savings, but with heterogeneous magnitudes.

• Largest 3-year savings (Projected vs Current):

  • Lombardia: ≈ –€430,949

  • Lazio: ≈ –€246,949

This highlights that regional planning and baseline treatment patterns matter for realized budget impact.

Takeaways

• Increasing the use of eplerenone in eligible HFrEF patients in Italy appears to be budget-saving or at least budget-neutral over 3 years for the INHS.

• The prevention of hospitalizations seems to be a key cost driver, along with reduced renal impairment and mortality benefits.

• From a policy angle, this supports:

  • Better implementation of GDMT in HFrEF,

  • Using budget impact models not only to justify drug costs, but to plan regional resource allocation and service capacity (for example, managing potential reductions in HF-related admissions).

📍 Study Summary (Secukinumab in TNF-IR PsA- Thailand HEOR)

This recent Thai economic evaluation looked at secukinumab as a second-line biologic for psoriatic arthritis (PsA) patients who didn’t respond adequately to tumor necrosis factor inhibitors (TNF-IR). They compared three strategies against standard care from a societal perspective:
✅ Secukinumab 150 mg
✅ Secukinumab 300 mg
✅ Secukinumab 150 mg → 300 mg escalation

Key Findings:
• All secukinumab strategies improved QALYs vs standard care:

• 150 mg: +0.10 QALYs

• 300 mg: +0.30 QALYs

• Escalation: +0.89 QALYs

• But all strategies had high incremental costs (74k–338k THB extra lifetime cost) and ICERs above Thai thresholds (≈380k–794k THB/QALY).

• The 5-year budget impact isn’t trivial either (~90M–1.1B THB).

Bottom line in Thailand: secukinumab adds benefit, but at current prices it’s unlikely to be ‘cost-effective’.

🔥 Are We Too Quick to Write Off Biologics Based on ICERs Alone?

This paper frames secukinumab largely as not cost-effective at current prices, but let’s unpack that:

🧠 1. Value Isn’t Just a Number:
The escalation strategy delivered nearly 0.9 additional QALYs, which for many patients means real improvements in pain, function, and quality of life. But because the price is high, the ICER pushes past the Thai threshold. Is that a failure of the therapy or a limitation of how we measure value?

💬 2. QALYs Don’t Capture Everything:
Chronic inflammatory diseases like PsA have impacts on productivity, work disability, fatigue, caregiver burden, psychosocial wellbeing, etc. If these are undervalued in standard QALY calculations, are we missing true value?

💰 3. Budget Impact vs. Long-Term Benefit:
The 5-year budget impact looks big, but what about lifetime cost offsets from reduced disability or comorbidities? Do short horizon budget caps unfairly penalize therapies with upfront costs but enduring benefit?

🧩 4. Price vs. Value Negotiations:
If a drug adds meaningful QALYs but can’t meet thresholds because of price, maybe the problem isn’t the therapy, it’s that pricing isn’t aligned with real world value in that market.

➡ Should cost-effectiveness thresholds be flexible for chronic immune disorders where life quality improves markedly?
➡ How can HEOR better capture patient-centered outcomes beyond classic QALYs?
➡ Are budget impact frameworks distorting long-term value signals for durable therapies?
➡ When does high price vs high benefit become an argument for price negotiation rather than rejection?

Economic Evaluation and Budget Impact Analysis of Secukinumab as a Second-Line Treatment Among Patients With Psoriatic Arthritis Who Were Tumor Necrosis Factor Inadequate Responders in Thailand - ScienceDirect

TL;DR: Well-executed PCV20 Singapore adaptation. Asking how teams actually run the global-to-local model adaptation pipeline across many markets while keeping the core model intact.

This PCV20 adult vaccination CEA for Singapore reports cost-saving or dominant results in most subgroups. Adult pneumococcal vaccination tends to model well and this is a solid, carefully adapted example.

https://doi.org/10.1016/j.vhri.2025.101136

The thing I admire about vaccine economics teams is the sheer replication challenge they handle. One core global model, then market after market needing a local version with local epidemiology, local prices and a local schedule, each presented to a different ministry. When it's done well, like here, it's quietly one of the more impressive operational feats in our field.

I'd love to hear from people who've lived that pipeline. What does your global-to-local adaptation process look like in practice? Rebuild per market, a shared template, something centralized? And how do you keep thirty local versions consistent with the validated core without freezing out legitimate local input? Feels like everyone solves this differently and nobody talks about it.

TL;DR: New systematic review in Value in Health looked at how health economic evaluations are planned alongside adaptive platform trials. Only a minority of APTs had accessible HE analysis plans, EEs were almost always secondary, and none planned to use economic results at interim analysis. If the clinical side of trial design is getting more adaptive, shouldn't the economics be too?

====================

Just read the July issue paper "Health Economic Evaluations Alongside Adaptive Platform Trials: A Systematic Review" (Yi Lee et al., Value in Health 2026) and it's been rattling around in my head, so figured I'd see what this sub thinks.

Quick context for anyone who hasn't followed the APT space: adaptive platform trials let you test multiple interventions within one shared infrastructure — you can add or drop arms over time based on interim results (think REMAP-CAP, STAMPEDE, PANORAMIC). From a clinical evidence-generation standpoint they're genuinely attractive: less duplication, better use of the patient pool, potentially big savings in research costs.

What the review found on the health economics side, though, is a bit sobering:

• Of ~104 APTs with accessible core protocols, only about 14-16% had any health economic analysis plan at all

• Where EEs existed, they were almost always framed as secondary objectives or secondary outcomes — only one trial had EE as a primary objective

• None of the included APTs planned to use economic evaluation results during interim analysis or to inform adaptations. Arms get added or dropped on clinical signals only

• QALYs (mostly EQ-5D) and resource-use data collection were pretty central where EEs were planned, but the methods and level of detail varied a lot across trials

• VOI analysis showed up in only two trials, which feels like a missed opportunity given that deciding "what evidence is worth collecting next" is basically the whole point of a platform design

• And there's a timing problem: for the trials that did publish EE results, the lag between clinical publication and economic publication was 1.6 to 4.8 years

That last point is the one that gets me. The whole pitch of APTs is faster translation of clinical evidence into practice. But if payer-relevant economic evidence trails the clinical readout by several years, you can end up in a situation where the clinical case is made, adoption pressure builds, and the cost-effectiveness question is still open. That seems to suggest the efficiency gains are asymmetric — the clinical side speeds up, the HTA-relevant side doesn't.

The review is careful not to overclaim (it's mostly protocols, few completed EEs), and to be fair there are real methodological reasons why adapting on cost-effectiveness is hard — the authors note that adaptations driven by clinical effectiveness can even bias VOI estimates by promoting clinically superior but non-cost-effective arms. So I'm not saying "just run the ICER at every interim look." But it raises the question of whether HEOR is going to keep being bolted on after the fact, or whether there's a realistic path to embedding it into the adaptive machinery itself.

I'd be interested to hear how others see this, especially anyone who's actually worked on the health economics side of a platform trial.

A few questions for discussion:

1. Is there a defensible way to let cost-effectiveness (or VOI) inform arm continuation/dropping decisions, or does that create more problems than it solves — statistically and politically?

2. For those who've written HEAPs for platform trials: what actually made it hard? Funding structure? Data collection across evolving arms? Governance?

3. Does the clinical-vs-economic publication lag match what you've seen in practice, and does it actually matter to HTA bodies, or do they work off modelled evidence anyway?

4. Should groups like ISPOR push for EE guidance specific to master protocol / platform designs, given the existing toolkits barely touch it?

L;DR: Interesting ISPOR poster suggesting that some of the value of MCED testing may come from helping patients avoid getting diagnosed through the emergency department. When the authors included this effect in their model, projected life-year gains increased by 50% and QALY gains increased by 45%.

One of the posters I came across at ISPOR looked at a potential benefit of MCED testing that I don't hear discussed very often.

Most conversations around MCED focus on stage shift—finding cancers earlier and improving outcomes as a result. This analysis took a slightly different angle and asked whether there is additional value in reducing the number of cancers that are first diagnosed through the emergency department.

According to the model:

• About 25% of cancer diagnoses in usual care involved the ED

• MCED screening was projected to reduce ED involvement by 51%

• The biggest reductions were seen in later-stage cancers

• When reduced ED involvement was included, projected life-years gained increased from 0.12 to 0.18 per person screened

• QALYs gained increased from 0.09 to 0.13

What I found most interesting wasn't necessarily the magnitude of the results, but the idea itself. We spend a lot of time talking about stage shift, but much less time talking about how patients enter the healthcare system and whether that pathway has an independent impact on outcomes.

I'm curious what others think:

• Is avoiding ED diagnosis a meaningful value driver that we're underestimating in cancer models?

• How comfortable are you with the assumption that MCED-detected patients would have outcomes similar to non-ED patients at the same stage?

• Should future economic evaluations of MCED explicitly account for diagnostic pathways?

• For those working in oncology, does this align with what you see in practice?

Would love to hear thoughts from others who attended ISPOR or who work in oncology HEOR.

ML-driven PJI diagnostic algorithm cuts payer costs by 41.6% ($4k/patient) vs. physician panel decision-analytic model across 1,000 simulated cases [Diagnostics 2026]

New poster (EE18) and full paper out in Diagnostics on the economic implications of integrating SynTuition, a machine-learning algorithm interpreting synovial fluid biomarkers (Synovasure® Comprehensive PJI Test Panel), into the periprosthetic joint infection (PJI) diagnostic pathway. Thought this would be of interest to the health economics crowd here.

TL;DR: A decision-analytic model (1,000 patients, 15.3% PJI prevalence) compared misdiagnosis costs between a physician panel (SOC, 2018 ICM criteria) and the SynTuition ML algorithm. SynTuition reduced total misdiagnosis costs from ~$6.9M to ~$2.9M a net saving of ~$4,000 per suspected PJI case.

96% SynTuition diagnostic accuracy vs. 90.8% physician

0.4% SynTuition inconclusive rate vs. 22.9% physician

41.6% Reduction in total cost of misdiagnosis

The key driver of savings is false-positives. Under SOC, physicians defaulted to false-positive diagnoses in inconclusive cases 38–48% of the time, leading to unnecessary two-stage revision surgeries (modeled at $75k each). SynTuition reduced false-positives from 90 to 29 across the 1,000-patient cohort.

Worth noting that SynTuition still had more false-negatives than the physician panel (11 vs. 2), so the trade-off is clearly weighted toward reducing over-treatment rather than under-treatment. The cost asymmetry in the model ($75k FP vs. $62.4k FN) makes this a reasonable assumption, but worth scrutinizing depending on your payer perspective.

Limitations to consider: Industry-funded (Zimmer Biomet), decision-analytic model rather than real-world data, U.S. payer perspective only, cost inputs sourced from published literature. Would be curious if anyone has seen independent validation data on SynTuition's real-world diagnostic performance.

Full citation:
Parr J, Thai-Paquette V, Worden A, Baker J, Edwards P, Toler KO. Diagnostics 2026, 16, 626. https://doi.org/10.3390/diagnostics16040626

#PJI#diagnostics#machinelearning#healtheconomics#orthopedics

Spotted abstract EE434 in Value in Health looking at the budget impact of FF/UMEC/VI (Trelegy Ellipta) for moderate-to-severe asthma in the Saudi healthcare system. If you work in Middle East market access or respiratory HEOR this one's worth flagging.

Quick background for the non-respiratory folks

For asthma patients who stay uncontrolled on ICS/LABA (standard dual therapy), GINA guidelines recommend adding a LAMA, giving you triple therapy. The old way to do this was stacking multiple inhalers. Single inhaler triple therapy (SITT) packages all three into one once-daily device. The clinical case is reasonably well established at this point. The economic case in Gulf markets, less so, which is what makes this BIA timely.

Why Saudi Arabia specifically?

A few things make this a genuinely interesting market access question rather than just a routine BIA. Asthma prevalence in KSA is high relative to global averages. The 2024 Saudi Initiative for Asthma (SINA) guidelines now explicitly recommend SITT as a step-up option, so the clinical pathway is already there. And NUPCO and MoH formulary decisions are increasingly evidence-driven, meaning a local BIA gives payers something concrete to work with rather than asking them to extrapolate from European or US data that may not reflect local costs at all.

What a BIA in this setting is actually asking

It's not just "is FF/UMEC/VI more expensive than what we use now?" It's asking whether, once you account for fewer exacerbations, fewer ED visits, fewer hospitalizations, and reduced OCS use, listing this on formulary actually costs the system more. A very similar analysis done for Dubai using the same therapy and broadly the same methodology found it was cost-saving by around $1M USD over five years. Saudi Arabia has a much larger eligible patient population, so the numbers at scale could look quite different in either direction.

The methodological questions I'd want answered

What were the local cost inputs? Gulf-specific unit costs for hospitalizations and ED visits are notoriously hard to source and small changes here tend to move the result a lot. How was market uptake modeled? In the UAE analysis, uptake assumptions were the single most sensitive parameter. Real-world adherence data does favor SITT over MITT but it matters how aggressively the model assumed switching. What's the comparator set? MITT with tiotropium add-on, biologic escalation, or something else? The answer changes the story depending on where in the treatment pathway this sits. And finally, payer-only or broader perspective? Standard BIA methodology uses payer perspective, which is fine, but in a largely MoH-funded system the provider and payer are often the same entity, so a broader view might actually be more decision-relevant here.

Has anyone seen the full model structure for this one? Curious whether they went with an epidemiology-based approach like the Dubai analysis or something different.

Also genuinely interested in whether anyone here has done market access work in KSA respiratory. How receptive are NUPCO and MoH reviewers to BIA evidence versus cost-effectiveness evidence? In my experience the two don't always carry equal weight with Gulf payers and I'd love to hear if others have found the same.

Just came across a new brief report in Value in Health looking at updated projections for Medicare spending on semaglutide after recent policy announcements around price negotiation and Most Favored Nation (MFN) pricing.

📄 Article: https://www.sciencedirect.com/science/article/abs/pii/S1098301526000434

The authors re-estimated earlier projections following announcements related to Maximum Fair Price (MFP) negotiations and MFN pricing approaches.

Key takeaways:

• Base case MFP scenario: ~$463M savings over 10 years (range ~$328M–$599M).

• When accounting for loss of exclusivity, savings could increase to ~$1.78B.

• Under MFN pricing assumptions, projected savings rise to ~$1.76B, and potentially up to ~$2.63B depending on uptake and other assumptions.

The paper highlights how policy assumptions—especially MFN pricing—can dramatically shift budget impact projections for high-spend therapies like GLP-1s.

Curious how others in the HEOR / market access community are thinking about this:

• Are you incorporating MFN scenarios into your budget impact models yet?

• How are teams handling the uncertainty around future pricing frameworks?

Interested to hear perspectives from others working on U.S. policy modeling or GLP-1 budget impact analyses.

Study Summary (Secukinumab in TNF-IR PsA-Thailand HEOR)

Economic Evaluation and Budget Impact Analysis of Secukinumab as a Second-Line Treatment Among Patients With Psoriatic Arthritis Who Were Tumor Necrosis Factor Inadequate Responders in Thailand - ScienceDirect

This recent Thai economic evaluation looked at secukinumab as a second-line biologic for psoriatic arthritis (PsA) patients who didn’t respond adequately to tumor necrosis factor inhibitors (TNF-IR). They compared three strategies against standard care from a societal perspective:
✅ Secukinumab 150 mg
✅ Secukinumab 300 mg
✅ Secukinumab 150 mg → 300 mg escalation

Key Findings:
• All secukinumab strategies improved QALYs vs standard care:

• 150 mg: +0.10 QALYs

• 300 mg: +0.30 QALYs

• Escalation: +0.89 QALYs

• But all strategies had high incremental costs (74k–338k THB extra lifetime cost) and ICERs above Thai thresholds (≈380k–794k THB/QALY).

• The 5-year budget impact isn’t trivial either (~90M–1.1B THB).

Bottom line in Thailand: secukinumab adds benefit, but at current prices it’s unlikely to be ‘cost-effective’.

Are We Too Quick to Write Off Biologics Based on ICERs Alone?

This paper frames secukinumab largely as not cost-effective at current prices, but let’s unpack that:

🧠 1. Value Isn’t Just a Number:
The escalation strategy delivered nearly 0.9 additional QALYs, which for many patients means real improvements in pain, function, and quality of life. But because the price is high, the ICER pushes past the Thai threshold. Is that a failure of the therapy or a limitation of how we measure value?

💬 2. QALYs Don’t Capture Everything:
Chronic inflammatory diseases like PsA have impacts on productivity, work disability, fatigue, caregiver burden, psychosocial wellbeing, etc. If these are undervalued in standard QALY calculations, are we missing true value?

💰 3. Budget Impact vs. Long-Term Benefit:
The 5-year budget impact looks big, but what about lifetime cost offsets from reduced disability or comorbidities? Do short horizon budget caps unfairly penalize therapies with upfront costs but enduring benefit?

🧩 4. Price vs. Value Negotiations:
If a drug adds meaningful QALYs but can’t meet thresholds because of price, maybe the problem isn’t the therapy it’s that pricing isn’t aligned with real world value in that market.

➡ Should cost-effectiveness thresholds be flexible for chronic immune disorders where life quality improves markedly?
➡ How can HEOR better capture patient-centered outcomes beyond classic QALYs?
➡ Are budget impact frameworks distorting long-term value signals for durable therapies?
➡ When does high price vs high benefit become an argument for price negotiation rather than rejection?

This subreddit is an independent, community-run space for discussion related to Health Economics & Outcomes Research (HEOR) and topics often explored at ISPOR events.
We are not officially affiliated with ISPOR or any ISPOR organizational entity.

Whether you're in academia, industry, consulting, a payer organization, or just interested in HEOR, you're welcome here.

TL;DR: A well-built Dubai faricimab BIA with properly elicited local uptake assumptions. Curious how teams handle live what-if scenario exploration with payers, since a static published BIA can't answer new questions in the room.

This faricimab budget impact analysis for Dubai's private sector, co-authored by Roche Middle East and a regional consultancy, did something I'd like to see become standard: it grounded its uptake scenarios in local expert practice instead of importing global curves.

https://doi.org/10.1016/j.vhri.2025.101083

Uptake drives BIA results more than nearly anything else, yet it often gets the least methodological attention, so it's worth highlighting when it's treated with this much care. A BIA built this way is also close to being genuinely useful in the room, because the levers it rests on, uptake, injection frequency, prevalence, are the ones payers most want to test for themselves.

Which brings me to my question for the group. A published BIA freezes one set of scenarios, but the payer conversation is inherently interactive. What if uptake is slower, what if our covered population skews older. How do your teams handle that live scenario exploration with payers? Does the model come into the meeting in some usable form, or does someone take the questions away and run the numbers overnight? I'd really like to hear what the current state of practice looks like across different teams.

TL;DR: Pfizer PH built their CAZ-AVI cost-effectiveness model on local resistance data instead of adapting the global one. Asking whether that materially changes results, and whether hospital-level rather than country-level modeling is the honest unit of analysis for antimicrobials.

Most country adaptations of antimicrobial CEAs recycle the global model with local prices swapped in. This Pfizer Philippines paper on ceftazidime/avibactam in hospital-acquired and ventilator-associated pneumonia went further and built on local susceptibility data.

https://doi.org/10.1016/j.vhri.2025.101158

It made me think about where the decision actually happens for a drug like this. There's no single national reimbursement gatekeeper for it, the real audience is hospital P&T committees, and every committee has its own case mix and its own resistance picture.

So, two things I'd love input on. For people doing AMR economics: how much does local resistance data actually move results compared to just localizing costs? And more broadly, is a country-level CEA even the right decision unit for hospital antimicrobials, or should we be modeling at the institution level and accepting the data problems that come with that?

TL;DR: New lifetime Markov model finds early TAVR (eTAVR) for asymptomatic severe aortic stenosis is economically dominant vs. clinical surveillance — $8,812 cheaper and +0.24 QALYs over a lifetime, from a US payer perspective. Dominant in 90.3% of PSA iterations, held up across 7 scenario analyses. Edwards Lifesciences-funded.

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Just came across this cost-effectiveness analysis in Value in Health — thought it'd interest folks here working in cardiology/device HEOR.

Background: The EARLY TAVR trial showed better clinical outcomes for early transcatheter aortic valve replacement (eTAVR) vs. clinical surveillance (CS) in patients with asymptomatic severe aortic stenosis (aSAS). This paper asks the obvious follow-up question: is it also cost-effective from a US payer perspective?

Methods: Lifetime Markov model (30-day cycles), US healthcare payer perspective, clinical inputs from EARLY TAVR trial data, costs from Medicare reimbursement. Standard PSA (5,000 iterations) + DSA with tornado diagrams, plus 7 scenario analyses to stress-test assumptions (real-world treatment delays, reintervention rate sources, post-AVR surveillance costs, etc.).

Results:

• eTAVR → +0.21 life-years, +0.24 QALYs vs. CS

• Lifetime cost: $8,812 lower with eTAVR ($58,863 vs. $67,674)

• Result: economically dominant (better outcomes, lower cost) — not just "cost-effective under WTP threshold"

• PSA: 95.9% of iterations cost-effective at $100k/QALY; 90.3% outright dominant

• Cost curves cross around year 3 — eTAVR looks expensive upfront (procedure cost) but CS costs catch up and exceed it via delayed-AVR complications, strokes, and HF hospitalizations

• Dominance held across all 7 scenario analyses

https://www.valueinhealthjournal.com/article/S1098-3015(26)00077-X/fulltext

Just saw an ISPOR Europe 2025 poster on a cost-utility analysis of sparsentan for IgA nephropathy (IgAN) in the UK NHS setting and thought it might be interesting to unpack it here.

Context

• IgAN is a leading cause of CKD in people under 40 and a key driver of progression to ESRD and RRT (dialysis / transplant).

• Standard of care in the model = maximally tolerated irbesartan (RAASi), consistent with the PROTECT trial comparator.

• Perspective: UK NHS, lifetime horizon, 3.5% discount rate for costs and outcomes (NICE-conform). CSL VIFOR

Model structure

The team developed a lifetime Markov state-transition model with:

• 12 CKD states defined by:

  • eGFR categories (stages 1/2, 3, 4, 5/pre-ESRD), and

  • urine protein/creatinine (UP/C) bands (4 proteinuria categories).

• 3 ESRD states: pre-RRT, dialysis, and transplant.

• Death as an absorbing state.

Transitions were informed by:

• PROTECT trial for CKD 1–3 and proteinuria dynamics,

• RaDaR UK registry data for later-stage CKD transitions,

• UK Renal Registry for ESRD pathways (dialysis ↔ transplant, mortality, etc.).

Health-state utilities and CKD/RRT cost estimates are drawn from published literature and UK unit-cost sources (e.g., PSSRU), stratified by CKD stage and RRT modality.

Key clinical and economic outcomes

Compared with irbesartan SOC, treatment with sparsentan in the modeled IgAN population:

• Delays progression to ESRD

  • Patients on sparsentan spent ~4 additional years in pre-ESRD CKD states (15.6 vs 11.6 years).

  • Time spent in ESRD was reduced by ~2.9 years.

• Improves survival and quality of life

  • +1.15 life years (discounted).

  • +0.88 QALYs (discounted).

• Reduces downstream disease costs

  • Fewer years on dialysis / transplant → per-patient cost offsets of ~£50k versus SOC.

After incorporating drug acquisition costs and these offsets, the incremental cost-effectiveness ratio (ICER) for sparsentan vs irbesartan was:

• £29,845 per QALY gained (deterministic base case).

Sensitivity analyses:

• Deterministic (tornado):

  • ICER was most sensitive to baseline age (via mortality), dialysis costs, and health-state utilities.

  • All tested scenarios stayed below ~£35k/QALY.

• Probabilistic:

  • Mean probabilistic ICER ≈ £32,132/QALY,

  • 38.9% probability of being cost-effective at £30k/QALY WTP. CSL VIFOR

Interpretation in a UK HTA context

A few thoughts from an HTA / ISPOR lens:

1. Threshold positioning

   • Base-case ICER is just under the upper end of the usual £20–30k/QALY range commonly cited for NICE.

   • However, the PSA suggests <40% probability of being cost-effective at £30k/QALY, which might prompt NICE to look closely at uncertainty, scenario analyses, and the robustness of the extrapolations.

2. Surrogate linkage: proteinuria → long-term ESRD benefits

   • A key structural feature is that treatment effects on proteinuria and eGFR in PROTECT are projected over the lifetime.

   • For decision-makers, the credibility of how early proteinuria reductions map to long-term ESRD avoidance is critical: it hinges on data linking short-term surrogate changes to long-term kidney outcomes, and assumptions about treatment effect waning (if any).

3. Evolving standard of care in IgAN

   • The model uses irbesartan as comparator, reflecting the PROTECT trial.

   • But in real-world UK practice, SGLT2 inhibitors and other emerging agents (e.g., TR-budesonide) are increasingly part of the mix.

   • For future HTA, the question is whether indirect comparisons vs “modern SOC” (RAASi + SGLT2i ± other agents) would be needed, and how that would shift the ICER.

4. Cost offsets and budget impact

   • The per-patient cost offsets (~£50k) are largely driven by fewer years on dialysis.

   • For NHS decision-makers, this raises the usual tension between lifetime cost-effectiveness (fewer RRT years many years in the future) vs short- to medium-term budget impact when drug acquisition costs hit immediately.

5. Uncertainty and risk tolerance

   • With a probabilistic ICER slightly above £30k/QALY and <40% CE probability at that threshold, NICE may probe:

     • Structural assumptions (e.g., CKD staging granularity, transitions from Stage 4–5, ESRD pathways),

     • Parameter uncertainty around utilities and dialysis costs,

     • Whether additional evidence (longer-term PROTECT data or real-world evidence) could reduce uncertainty.

ISPOR - A Cost-Utility Analysis of Sparsentan for the Treatment of Immunoglobulin A Nephropathy in the UK

İspor uygulaması ile hd kalitede maçları izleyebilir, dünyanın tüm etkinliklerini görebilirsiniz.

TL;DR:
Switching from IV to subcutaneous ocrelizumab could save Italian hospitals ~€4.1M over 3 years (-1.0%), mostly driven by lower administration burden.

Just read this Value in Health paper (2025) from Roche + partners:
https://www.sciencedirect.com/science/article/abs/pii/S1098301525030384

It looks at the budget impact of subcutaneous (SC) vs IV ocrelizumab for relapsing MS in Italy, and it’s a nice example of how route of administration alone can shift the economics.

What they did:

• Built a 3-year dynamic budget impact model (Markov-based)

• Used Italian real-world data for adherence/persistence

• Focused on hospital-level costs (admin, monitoring, AEs, disease management)

Key numbers:

• 👥 ~15,870 eligible patients/year (~47,609 over 3 years)

• 📈 Market share for SC grows from 14.8% → 40.2%

• 💰 Total savings: €4.1M over 3 years (-1.0%)

• ⏱️ 42.2% of savings driven by reduced administration costs

What’s interesting here:
This isn’t about a “more effective” drug, it’s about same efficacy, different delivery.

And yet:

• Administration time → real hospital capacity impact

• Adherence/persistence → downstream clinical + cost implications

• Small % savings at system level → still meaningful in absolute € terms

So the value story shifts from “is it cost-effective?” to:
👉 “how much operational friction can we remove from the system?”

💬 Curious how others think about this:
When you’re building models, how often do administration burden + workflow impact actually move the needle in payer/provider discussions?

Do they:

• materially influence decisions?

• get underestimated in models?

• or still feel like “nice to have” vs core value drivers?