Oncology economic modeling often struggles with estimating clinical benefits like overall survival (OS) and progression-free survival (PFS), especially with short-duration trials involving extrapolating survival curves. This study analyzed methods used by the Brazilian National Committee for Health Technology Incorporation (CONITEC) in health technology assessment (HTA) to adapt economic models to evolving healthcare needs.

A descriptive analysis was conducted on the economic evaluations from CONITEC’s recommendation reports on oncology drugs published between January 2019 and December 2023. Three independent reviewers extracted data on outcomes, survival curve distributions, validation methods, and time horizons. The study focused on identifying patterns in modeling practices and explored whether these methods integrated with the expanding scope of HTA, including the use of non-traditional data sources and adaptive techniques.

Thirty CONITEC reports were analyzed, evaluating 32 oncology treatments across 45 indications. The most common economic models were partitioned survival analysis (PartSA) (58%) and Markov models (33%). The Weibull distribution was the most frequent in OS extrapolations (50% for PartSA; 84% for Markov). PFS extrapolations showed a preference for exponential distribution in PartSA (35%) and Weibull in Markov models (54%). Methods for model validation were reported in 69 percent of Markov models and 100 percent of PartSA models. Extrapolation horizons ranged from 10 years (75% of models) to 30 years (31%).

The analysis highlighted the reliance on traditional models, such as PartSA and Markov, for modeling long-term cancer progression. However, these models may not fully capture treatment complexity. Emerging methods and the integration of diverse data sources, such as real-world data, are vital for improving HTA relevance and addressing the evolving demands of healthcare systems and next-generation evidence.

Hereditary breast and ovarian cancer, linked to BRCA gene mutations, poses significant health risks. Australian breast and ovarian cancer mortality rates emphasize the public health importance of effective screening. This study evaluated the cost effectiveness of population-wide BRCA screening and its equity implications, focusing on reducing health disparities across socioeconomic groups through early detection and prevention strategies.

A cost-effectiveness analysis was conducted comparing population-wide BRCA screening to family-history-based testing for Australian women aged 30 to 50 years. A decision-analytic model included a decision tree and two Markov models simulating outcomes over a lifetime horizon. Transition probabilities captured risk-reducing interventions. Outcomes included quality-adjusted life years (QALYs) and costs in 2024 AUD, discounted at five percent. Socioeconomic quintiles were defined using Socio-Economic Indexes for Areas, and distributional impacts were assessed with net health benefits and equally distributed equivalent health (EDEH) using the Atkinson index. Model inputs were derived from census data and empirical studies. Opportunity costs used UK data as a proxy.

Screening at the age of 30 years was cost saving (incremental cost-effectiveness ratio [ICER] −AUD8,331 [−USD5,482] per QALY), while screening at ages 35 and 40 years was cost effective (ICERs: AUD28,231 [USD18,577] and AUD40,086 [USD26,384] per QALY, respectively). Screening at ages 45 and 50 years exceeded the threshold (ICERs: AUD78,666 [USD51,778] and AUD155,937 [USD102,640] per QALY). Distributional analysis showed screening at the age of 30 years increased average quality-adjusted life expectancy and EDEH, reducing health inequality between socioeconomic groups. In contrast, screening at older ages resulted in diminished benefits and exacerbated inequalities, highlighting the importance of early intervention in achieving both cost-effectiveness and equity goals.

Population-wide BRCA screening was cost effective for Australian women at younger ages, particularly at the age of 30 years, and reduced health inequalities. Policymakers should prioritize early screening to address hereditary breast and ovarian cancer, promote health equity, and alleviate disease burden. Further strategies to enhance access among disadvantaged groups could amplify these benefits, advancing a more equitable healthcare system.

Health technology assessment (HTA) often involves complex cost-effectiveness analyses (CEA) that can be challenging for non-experts. This study demonstrated the use of artificial-intelligence (AI)-powered prompts to simplify CEA processes. By integrating tools for generating efficiency frontiers and net-benefit analyses, this approach enables stakeholders, such as non-modeling HTA specialists or decision-makers, to understand therapeutic scenarios and make informed adjustments to analyses.

The prompts were created using ChatGPT-4o and tested for usability and reproducibility in ChatGPT-4o and the free version 3.5. The prompts were designed to automate key steps in economic analysis, including calculating net monetary and health benefits, performing cost-effectiveness analyses applying dominance and extended dominance concepts, and generating the efficient frontier plot. Ten HTA experts with no modeling experience evaluated the prompts using predefined scenarios with hypothetical datasets. The results of both versions of ChatGPT were compared to the expected results. The usability and accuracy of the prompts were assessed during the evaluation.

ChatGPT-4o achieved 100 percent accuracy in calculating net health and monetary benefits (NHB and NMB) and correctly applied the concepts of dominance and extended dominance in 83 percent of cases. It calculated the incremental cost-effectiveness ratio (ICER) for non-dominated therapies in 50 percent of situations and successfully generated the efficiency frontier graphic in half of cases. In contrast, ChatGPT 3.5 achieved 50 percent accuracy for NHB and NMB calculations, only 17 percent for applying dominance and extended dominance concepts, and failed to calculate ICERs or generate the efficiency frontier plot as expected.

AI-powered prompts simplified cost-effectiveness analyses by enabling non-technical stakeholders to create and adjust efficiency frontiers and benefit analyses. ChatGPT-4o demonstrated improved reliability. Limitations in ChatGPT 3.5, particularly with ICER calculation and graphical outputs, indicated the need to adapt prompts to specific AI tools. Future developments may increase the robustness and usability of these types of prompts across platforms.

Generative artificial intelligence (AI) holds promise in aiding development of health economic models. Our objective was to explore the feasibility of using generative AI to replicate health economic models based on previously published models.

We replicated a Markov model of ulcerative colitis described in the literature using a two-step approach. First, we used Python for large language model interactions and utilized ValueGen.AI, a GPT-4-based platform with multi-agent pipelines (CrewAI, LangChain, and OpenAI libraries), to extract model structure and parameters from the source. These parameters were implemented in R’s heemod package to construct and run the Markov model. Next, we repeated the experiment using a more detailed technical report of the same model. We evaluated generative AI’s performance by comparing its conceptualization and parameterization with the original sources.

Using the publication, the generative AI platform effectively extracted costs and quality-of-life inputs linked to health states. However, for health states and transition probabilities, initial attempts were less successful due to limited descriptions in the text, resulting in misinterpretations and conflicting health states. Performance improved considerably when we used the detailed technical report, which offered clearer and more structured information. Generative AI could not successfully extract all transition probability calculations for the defined model cycle length based on the formulas provided in the documents.

Generative AI holds significant potential for replicating previously published health economic models. However, challenges remain in capturing detailed model parameters, particularly when description of modeling approach lacks clarity and transparency. Improving standardized reporting practices within the health economics and outcomes research field is needed to enable generative AI to better support stakeholders during HTA processes.

Liquid biopsy (LB) is a minimally invasive technique that enables analysis of circulating tumor DNA (ctDNA) by next generation sequencing (NGS) and has several potential applications in oncology. The main objective was to describe the diagnostic accuracy of LB over tissue biopsy for screening, diagnosis, and tumor mutation profiling in breast, lung, and colorectal cancer, including at different cancer stages.

Initially, a literature overview to select systematic reviews with meta-analyses or health technology assessment reports was conducted. When no data were obtained, a systematic review was conducted to select individual studies. For both procedures, a systematic literature search, two-step study selection, quality appraisal and a narrative synthesis were conducted.

The only study in the screening setting involved colorectal cancer (sensitivity 14%, specificity 90%). Regarding initial cancer diagnosis, LB in breast cancer had a sensitivity ranging from 31 to 74 percent and a specificity of 66 to 92 percent, with inconsistent results by stage. For lung cancer, LB had a sensitivity of 22 to 86 percent and a specificity of 42 to 91 percent, with improved utility in advanced stages. Colorectal cancer studies using multicomponent tests showed sensitivities of 83 to 93 percent and specificities of more than 95%. LB ctDNA analysis in molecular profiling of advanced cancer had high accuracy in breast (PIK3CA), lung (epidermal growth factor receptor mutations), and colorectal cancer (NRAS/KRAS), with values for sensitivity of up to 83 percent and for specificity of around 90 percent.

No results were available for screening in breast and lung cancer. Limited evidence for colorectal cancer showed low sensitivity but high specificity for LB over tissue biopsy. Initial cancer diagnosis results were heterogeneous, with inconsistencies in breast and colorectal stages but improvements in advanced lung cancer. Tumor profiling provided the strongest evidence, with high diagnostic utility relative to tissue biopsy across all three cancer types.

Hospital malnutrition is caused by metabolic imbalance and is associated with increased rates of morbidity and mortality and unfavorable prognosis for hospitalized patients. In 2001, severe malnutrition was detected in 12.5 percent of hospitalized patients in Brazil. The aim of this study was to evaluate evidence on the efficacy and safety of oral nutritional supplements for malnutrition in hospitalized patients.

Search strategies developed for the PubMed, Embase, LILACS and Cochrane Library databases were used to search and select the available evidence. The included evidence compared oral nutritional supplementation with standard hospital care or placebo. A total of 47 clinical trials were selected. For mortality, a meta-analysis was performed combining the effect measures of 40 clinical trials. R software was used with the meta package installed. The revised Cochrane risk-of-bias tool was used to assess the risk of bias.

For the primary outcomes of mortality (relative risk [RR] 0.94, 95% CI: 0.77, 1.15), length of hospital stay, and number of hospital readmissions (RR 0.97, 95% CI: 0.74, 1.28), oral supplementation offered no benefit, compared with the control group. Regarding infectious and non-infectious complications, there was a significant reduction in the number of patients with complications in the supplemented group, compared with the control group (RR 0.71, 95% CI: 0.59, 0.86). Regarding the risk-of-bias assessment, most of the studies had a high risk of bias and limitations in almost all areas.

The designs of the selected studies were quite heterogeneous in terms of follow-up time, population, and sampling, among others, and several doubts were raised about the methodology used in the clinical trials. These aspects were reflected in the low quality and high risk of bias of the primary studies, which proved to be an important limitation.

Generative artificial intelligence (AI) is transforming the generation of real-world evidence in health care. This study compares chemotherapy recommendations for women with node-positive breast cancer, as provided by a Delphi panel of experts, with those generated by ChatGPT and Copilot. The objective was to assess concordance and discrepancies between AI-generated and expert-driven recommendations.

A Delphi panel of 10 independent breast cancer experts, blinded to each other’s responses, participated in an online survey to evaluate chemotherapy recommendations for early stage, node-positive breast cancer, stratified by menopausal status and Oncotype DX Breast Recurrence Score® (RS: <14, 14 to 25, >25). ChatGPT and Copilot, using automatic prompt engineering, addressed the same scenarios as agent-based oncologists. Expert recommendations were summarized as arithmetic means, while AI responses were analyzed for concordance. A one-sample t-test compared mean estimates between the Delphi panel and each AI tool.

Chemotherapy recommendation rates for premenopausal women by the Delphi panel, ChatGPT, and Copilot were: RS less than 14 (82.2, 2.5, 10); RS 14 to 25 (96.7, 22.5, 30); RS greater than 25 (100, 80, 60). For postmenopausal women, these were: RS less than 14 (3.3, 1.5, 15); RS 14 to 25 (17.8, 17.5, 35); RS greater than 25 (94.4, 72.5, 65). Significant differences were observed, especially in lower RS ranges. Differences in means between the Delphi panel and AI for postmenopausal with a RS greater than 25 and premenopausal with a RS less than or equal to 25 were statistically significant (p<0.05).

The recommendations from AI demonstrated significant divergence from the “gold standard” panel, especially for premenopausal patients. The observed deviations from Brazilian experts’ recommendations likely stem from differences in local practices, compared with international published data, which may have influenced AI outputs. These findings emphasize the continued importance of human panels to account for regional variations in clinical decision-making.

HIV-1 compromises the immune system, leading to immunodeficiency. Standard treatment involves the use of antiretroviral therapies; however, the development of resistance is common, highlighting the need for alternative therapeutic options. This study evaluated the therapeutic value and cost effectiveness of fostemsavir in combination with optimized background therapy (OBT), compared with OBT alone for patients living with multidrug-resistant HIV-1.

A cost-effectiveness analysis was conducted to estimate the incremental cost-effectiveness ratio (ICER) of fostemsavir combined with OBT for people living with HIV-1 resistant to at least four classes of antiretroviral therapies, compared with OBT alone. The analysis was performed from the perspective of the Brazilian Unified Health System (SUS), considering a one-year treatment horizon. For OBT, it was assumed that the regimen could include the simultaneous use of up to six medications. The efficiency frontier methodology was applied to determine a proportional price based on the cost and therapeutic response benefit.

Fostemsavir demonstrated an incremental effectiveness of 54 percent in therapeutic response rate (viral load reduction). Over a one-year horizon, the cost-effectiveness analysis revealed a cost of BRL163,402.21 (USD27,233.70) for fostemsavir combined with OBT, compared with BRL23,271.60 (USD3,878.60) for OBT alone. The incremental cost of using fostemsavir was BRL260,127.37 (USD43,354.56) per therapeutic response. Based on the efficiency frontier approach, the incremental cost of fostemsavir should not exceed BRL19,819.83 (USD3,303.30), calculated based on the amount currently spent by the SUS plus the 54 percent additional benefit.

The use of fostemsavir combined with OBT demonstrates a superior virologic response rate, compared with OBT. Efficiency frontier analysis suggested a maximum justified cost, providing a framework for pricing decisions. This study highlighted the importance of balancing innovation with cost effectiveness in healthcare decision-making.

Generic medicines form the backbone of affordable health care in low- and middle-income countries (LMICs), constituting a significant portion of pharmaceutical consumption. However, issues such as fragmented pricing policies and insufficient regulatory frameworks hinder equitable access and affordability. This study evaluated pricing models that address these challenges by integrating best practices, including tiered pricing mechanisms and enhanced transparency. The aim was to guide policymakers toward sustainable solutions that improve medicine affordability, equity, and market resilience.

A comparative analysis was conducted across 20 countries, chosen for their diverse healthcare profiles and potential to be best practice examples for LMIC contexts. Key elements analyzed included pricing mechanisms (e.g., tiered pricing, external reference pricing, internal reference pricing), regulatory frameworks, and incentives for local production. Data were gathered from government reports, healthcare provider feedback, and industry insights. A policy evaluation framework was applied to assess each model’s effectiveness in addressing affordability, accessibility, and equity. The analysis prioritized generalizability to LMICs while accounting for contextual adaptations such as regional economic challenges and varying levels of regulatory capacity.

Supported by TUBITAK 2224-A Program for participation in HTAi 2025; it had no role in the study conduct.

The findings revealed that tiered pricing structures significantly enhanced affordability by incentivizing market competition and reducing prices as generics enter. Transparent regulatory practices, including centralized databases and consistent price reviews, mitigated regional price disparities and increased trust among stakeholders. Countries fostering domestic production reduced import dependencies, stabilizing supply chains and ensuring medicine availability. These strategies, when applied collectively, have great potential to improve equitable access to generics in resource-constrained settings. The results highlighted the importance of adapting global best practices to the economic and healthcare realities of LMICs, emphasizing sustainability and resilience in pharmaceutical markets.

Integrating tiered pricing frameworks, regulatory transparency, and local production incentives provided a pragmatic pathway to improve generic medicine access in LMICs. These approaches align with global best practices and address affordability and equity challenges. Policymakers should prioritize such strategies while leveraging stakeholder collaboration. Future research should examine dynamic pricing models and their long-term impacts on market stability and healthcare outcomes.

Impaired social functioning is commonly observed in youth at clinical high risk (CHR) for psychosis. Interpersonal synchrony, defined as the temporal alignment of movement between interacting partners, is a key component of successful social interactions. This study aimed to investigate interpersonal head synchrony in naturalistic virtual settings among CHR individuals using automated video analysis tools.

We analyzed short video recordings from virtual clinical interviews involving 116 participants including 50 CHR participants, 36 individuals with sub-threshold positive symptoms (SUB), and 30 healthy controls (HC). Vertical head movement time series were extracted using an open-access video-based head-tracking tool. Interpersonal head synchrony was computed using Windowed Cross-Correlation to assess group differences and associations with clinical symptoms and functioning.

CHR participants showed significantly reduced strength of synchrony compared to HC (β = −0.05, 95% CI [−0.09, −0.02], p = .004), although 14% of variance in strength of synchrony was attributable to assessor identity. No significant group differences were found for delay of synchrony. Within the CHR group, delay of synchrony was positively associated with social anhedonia (r = 0.29). Strength of synchrony correlated with better social (r = 0.33) and role (r = 0.28) functioning.

Our findings suggest that impaired interpersonal head synchrony is already present in the psychosis-risk state and relates to negative symptoms and social and role functioning. These findings support the utility of nonverbal synchrony as a potential biomarker and demonstrate the feasibility of automated tools and virtual assessments to study social processes in at-risk populations.

Head and neck squamous cell carcinoma (HNSCC) is the most common malignancy of the oral cavity, pharynx, and larynx. Nivolumab has demonstrated significant survival benefits in patients with recurrent or metastatic HNSCC after platinum-based chemotherapy. This study evaluated the cost utility of nivolumab compared with standard chemotherapy for recurrent or metastatic HNSCC in adults in the Brazilian Unified Health System (SUS).

A partitioned survival model was developed using survival data from the CheckMate-141 trial. Health states included progression-free survival (PFS), post-progression survival, and death. Estimates for overall survival (OS) and PFS were extrapolated from Kaplan-Meier curves using parametric distributions. Costs and utilities were analyzed from the SUS perspective over a five-year horizon, with a five percent annual discount rate for both costs and benefits. Utilities were derived from international literature. Costs were measured in BRL and converted to purchasing-power parity in 2023 (USD1=BRL2.44). Incremental cost-utility ratios (ICUR) were calculated using quality-adjusted life years (QALYs) as the outcome.

Nivolumab resulted in incremental costs of USD56,040 (BRL136,737) and incremental benefits of 0.12 QALYs and 0.37 life years gained (LY), compared with chemotherapy, leading to an ICUR of USD458,039 (BRL1,117,615) per QALY gained, or USD152,588 (BRL372,315) per LY. This exceeded the commonly accepted threshold of USD49,180 (BRL120,000) per QALY for the SUS. Sensitivity analyses identified drug costs and utility values as the most impactful parameters. Despite benefits for OS (hazard ratio 0.68, 95% confidence interval [CI]: 0.54, 0.86), nivolumab was not cost effective under current thresholds due to its high costs and lack of effect on PFS (hazard ratio 0.89, 95% CI: 0.70, 1.13).

While nivolumab offered significant clinical benefits, including improved OS and a favorable safety profile, its high incremental cost means it is not a cost-effective option for treating recurrent or metastatic HNSCC from the SUS perspective. Strategic pricing adjustments or alternative access mechanisms may be necessary to enable broader access to this drug in the Brazilian healthcare system.

Visceral leishmaniasis (VL), caused by Leishmania infantum, is a significant public health challenge in the Americas and the Mediterranean basin. Effective disease control requires timely and accurate diagnostic methods, particularly in endemic regions. This study systematically evaluated the diagnostic accuracy of polymerase chain reaction (PCR), the rK39 rapid test, and ELISA with respect to their sensitivity and specificity against parasitological examination across diverse populations.

A systematic review was conducted using MEDLINE, Embase, the Cochrane Library, and LILACS databases without restrictions on publication date, language, or whether the study was published, unpublished, or ongoing. Studies assessing the diagnostic accuracy of PCR, the rK39 rapid test, and ELISA for detecting VL against direct parasitological examination were included based on predefined PIROS framework (Population, Index test, Reference standard, Outcomes, and Study design). Study selection and data extraction were independently performed by two reviewers, with disagreements resolved by a third reviewer. Data were synthesized narratively and through meta-analyses to estimate sensitivity and specificity. Methodological quality was assessed using QUADAS-2 and evidence certainty was evaluated using the GRADE approach.

Twenty studies were included. In immunocompetent patients, PCR had high sensitivity (91 to 98.5%) and specificity (up to 100%) for detecting VL, providing a less invasive alternative to bone marrow aspiration. The rK39 test had sensitivity of 82.4 to 97 percent and a specificity close to 100 percent, which is suitable for endemic regions. ELISA sensitivity ranged from 89 to 94 percent. In immunosuppressed patients, PCR retained superior accuracy (83 to 100% sensitivity), while the rK39 sensitivity declined to 60 to 67 percent. Certainty of the evidence was moderate for immunocompetent patients but low for immunosuppressed groups, due to heterogeneity and the limited number of studies.

PCR was the most accurate method for detecting Leishmania infantum in endemic regions, especially for immunosuppressed patients. The rK39 rapid test was effective for initial screening in high-prevalence areas, and ELISA was helpful in specific contexts. These results emphasize the need for non-invasive, accurate diagnostics. Future studies should address evidence limitations, particularly for vulnerable populations.

To explore nurses’ perceptions regarding their knowledge, degree of autonomy, and the difficulties encountered in managing diabetic foot in Primary Care.

Diabetes mellitus is a chronic condition with a high prevalence in Spain, predominantly type 2. One of its most serious complications is diabetic foot disease, affecting between 19% and 34% of patients and associated with considerable morbidity and amputation risk. Primary Care, particularly nursing professionals, plays a pivotal role in the prevention, assessment, and management of diabetic foot. However, institutional, methodological, and personal barriers continue to affect care quality.

A descriptive, cross-sectional observational study was conducted using quantitative and qualitative methods. A validated ad hoc questionnaire was administered to 176 nurses from the Murcian Health Service participating in a blended learning course on diabetic foot. Variables assessed included professional autonomy, knowledge, dressings use, clinical documentation, training, and perceived challenges. Qualitative analysis was based on open-ended responses using content analysis.

A total of 88.1% of nurses reported autonomy in performing foot examinations; however, only 45.5% managed wound care independently. Just 19.9% considered themselves sufficiently trained, while 42.6% felt confident in selecting dressings appropriate to the healing phase. Although 56.8% regularly completed specific clinical documentation forms, many still expressed uncertainty about dressing use. Qualitative analysis identified five key barriers: lack of knowledge, patient complexity, institutional constraints, issues of authority and communication, and professional insecurity. These findings provide a current picture of persistent barriers in diabetic foot care and reinforce the need for targeted training and institutional support.

In Brazil, decisions regarding the incorporation of health technologies, guided by health technology assessment (HTA), are made independently within the public and private health systems. There is ongoing discussion about establishing a unified HTA agency to address incorporation across both sectors. This study sought to compare the decision-making criteria for technology incorporation in Brazil’s public and private health systems.

A dataset of new medicines registered in Brazil between 2010 and 2020, along with their corresponding indications, was compiled to identify and assess medication-indication pairs. Case studies were developed for pairs that underwent coverage evaluations in both the public and private health systems. These HTA coverage decisions were systematically analyzed and compared using a published evidence-based methodological framework. This methodology requires that research questions from different HTA agencies be sufficiently alike to enable a meaningful comparison of decision-making criteria.

Twenty medicines across 10 indications were analyzed. Oncology related pairs (10 medicines, five indications) could not be compared due to differing research focuses: public evaluations covered entire treatment lines, whereas private assessments focused on specific medicine-indication pairs. Non-oncology cases (10 medicines, five indications) targeted eosinophilic asthma, ulcerative colitis, psoriasis, multiple sclerosis, and neuronal ceroid lipofuscinosis type 2. Public sector decisions prioritized unmet needs, disease impact, clinical benefits, and negotiated prices. In contrast, the private sector focused on expanding therapeutic options, with clinical uncertainties having limited influence. The private sector did not consider costs in decision-making, even when medicines lacked clinical superiority over comparators.

Decision-making in the public sector relies on HTA principles, while the private sector focuses on therapeutic expansion, ignoring efficiency and clinical uncertainties. A unified HTA agency in Brazil could negatively affect the public sector if private sector priorities dominate. Moreover, such an agency is unlikely to reduce the workload, as research questions differ significantly across sectors.

The world is facing an urgent environmental emergency that calls for ambitious, coordinated action by governments to improve humankind’s relationship with nature. Mitigation (reducing pollution) and adaptation (adjusting to pollution) are both necessary, and “NextGen” health technology assessment can play a key role in helping to guide health system decision-makers to achieve environmental sustainability.

In 2022, the International Network of Agencies for Health Technology Assessment (INAHTA) identified environmental impact assessment (EIA) as an important and urgent topic for a white paper. An international author group (n=10), formed of staff from INAHTA member agencies, wrote the paper. The author group met seven times over 2022 to 2024 to develop the paper, which is based on a literature review and expert opinion of INAHTA members. A second group of members formed an international advisory group (n=11), which reviewed the draft twice. The paper was approved by the INAHTA Board before release on the INAHTA website.

EIA can be useful to help achieve the “green” policy goals of health systems by identifying technologies that are comparatively harmful to the environment. However, health technology assessment (HTA) agencies are subject to the institutional structures and regulations of their local health system that can constrain their ability to independently adapt to incorporate EIA. Nevertheless, HTA agencies are starting to do this with some success, but difficult challenges remain, particularly around lack of data and methods. Looking ahead, raising awareness and working together across the HTA ecosystem will be key to achieving the shared goal of environmental sustainability.

The successful inclusion of EIA in priority setting, assessment, and appraisal, as well as knowledge sharing and dissemination activities of HTA, is a challenge since the operating principles, methods, and data in this area are not yet mature. The successful inclusion of environmental impacts in HTA will require some reconsideration of existing value frameworks and methods.