Dalia Dawoud

OBJECTIVES: Ataluren and eteplirsen are orphan drugs that delay progression of Duchenne muscular dystrophy in mutation-specific subgroups. They have yet to be approved in Egypt but are expected to reach the market soon. This study describes 2 cost-utility models comparing the drugs with the standard of care.

METHODS: We used a partition-survival model with 5 states based on the ambulatory status to model a cohort of ambulatory patients at the age of 5 years. Baseline curves were obtained from a published model; then the ambulation loss curve was updated using the Kaplan-Meier curve of the standard of care from a study by McDonald et al. Other curves were updated by calibration to this curve. Costs and utilities were from a local study. Deterministic and probabilistic sensitivity analyses were conducted. Prices were estimated based on other orphan drugs' prices.

RESULTS: In the base case, ataluren 1000 mg and eteplirsen 50 mg/mL resulted in an incremental cost-effectiveness ratio of EGP 51 745 605 and EGP 69 652 533/quality-adjusted life-year, respectively, at their hypothetical prices of EGP 308 600 for ataluren 30-sachet pack and EGP 62 800 for eteplirsen 10 mL vial. The incremental cost-effectiveness ratio was sensitive to health state utilities but not to state costs. At EGP 911 719/quality-adjusted life-year threshold, the value-based prices were EGP 4680 for ataluren 1000 mg and EGP 733 for eteplirsen 10 mL vial.

CONCLUSIONS: Based on these models, there is a huge gap between the prices of orphan drugs and their value-based prices, which highlights the need for major policy reforms in the assessment and pricing of orphan drugs.

Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17 June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.

OBJECTIVES: Duchenne muscular dystrophy (DMD) is a rare neuromuscular disease that causes substantial economic burden. This study aims to measure the DMD cost from societal perspective and the quality of life (QOL) of the Egyptian patients.

METHODS: We conducted interviews with caregivers of patients with DMD. The questionnaire included demographics, healthcare resource use, and nonmedical and indirect costs. Total disease burden was estimated with a bottom-up approach. QOL was measured with a disease-specific tool. Costs and utilities were stratified by the disease stage.

RESULTS: Caregivers of 97 patients with DMD were interviewed. The mean annual per-patient cost of $17 485 (SD ± 9240) was estimated resulting in a total burden of $138 217 043 in Egypt. Nonmedical costs made up the largest category representing 54% followed by medical then indirect costs. Informal care made the greatest contribution of nonmedical costs whereas physiotherapy was the largest medical subcategory. Nonmedical costs were highest in stage 3 and lowest at early stages whereas medical costs were almost steady among all stages with differences in individual subcategories. Of all medical costs, 95% were out of pocket. The mean utility score was 0.43 (± 0.31), which decreases with disease progression.

CONCLUSION: Our study quantified the huge economic burden of DMD on the society and how it differs in different stages. Almost the whole burden is paid by households resulting in catastrophic expenditures, which leads to reduced compliance and quality of care. QOL is also severely compromised. Our findings can inform future healthcare policies and economic evaluation of new DMD therapies.

Real-world data and real-world evidence (RWE) are becoming more important for healthcare decision making and health technology assessment. We aimed to propose solutions to overcome barriers preventing Central and Eastern European (CEE) countries from using RWE generated in Western Europe. To achieve this, following a scoping review and a webinar, the most important barriers were selected through a survey. A workshop was held with CEE experts to discuss proposed solutions. Based on survey results, we selected the nine most important barriers. Multiple solutions were proposed, for example, the need for a European consensus, and building trust in using RWE. Through collaboration with regional stakeholders, we proposed a list of solutions to overcome barriers on transferring RWE from Western Europe to CEE countries.

The European Commission's Innovative Medicines Initiative (IMI) has funded many projects focusing on neurodegenerative disorders (ND) that aimed to improve the diagnosis, prevention, treatment and understanding of NDs. To facilitate collaboration across this project portfolio, the IMI funded the "NEURONET" project between March 2019 and August 2022 with the aim of connecting these projects and promoting synergies, enhancing the visibility of their findings, understanding the impact of the IMI funding and identifying research gaps that warrant more/new funding. The IMI ND portfolio currently includes 20 projects consisting of 270 partner organizations across 25 countries. The NEURONET project conducted an impact analysis to assess the scientific and socio-economic impact of the IMI ND portfolio. This was to better understand the perceived areas of impact from those directly involved in the projects. The impact analysis was conducted in two stages: an initial stage developed the scope of the project, defined the impact indicators and measures to be used. A second stage designed and administered the survey amongst partners from European Federation of Pharmaceutical Industries and Associations (EFPIA) organizations and other partners (hereafter, referred to as "non-EFPIA" organizations). Responses were analyzed according to areas of impact: organizational, economic, capacity building, collaborations and networking, individual, scientific, policy, patient, societal and public health impact. Involvement in the IMI ND projects led to organizational impact, and increased networking, collaboration and partnerships. The key perceived disadvantage to project participation was the administrative burden. These results were true for both EFPIA and non-EFPIA respondents. The impact for individual, policy, patients and public health was less clear with people reporting both high and low impact. Overall, there was broad alignment between EFPIA and non-EFPIA participants' responses apart from for awareness of project assets, as part of scientific impact, which appeared to be slightly higher among non-EFPIA respondents. These results identified clear areas of impact and those that require improvement. Areas to focus on include promoting asset awareness, establishing the impact of the IMI ND projects on research and development, ensuring meaningful patient involvement in these public-private partnership projects and reducing the administrative burden associated with participation in them.

During the coronavirus disease 2019 (COVID-19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID-19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta-analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID-19. Male sex was associated with higher risk of short-term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID-19 disparities were rarely reported. This review highlights key prognostic factors of COVID-19, which can help clinicians and health officers identify high-risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology.

BACKGROUND: Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries.

METHODS: We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report.

RESULTS: Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure.

CONCLUSION: In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.

Decision-making for reimbursement and clinical guidelines (CGs) serves different purposes although the decision-criteria and required evidence largely overlap. This study aimed to assess similarities and discrepancies between health technology assessment (HTA) reports as compared to CGs for multiple sclerosis (MS) medicines. All HTA reports and corresponding CGs for MS from the UK, France, Germany, the Netherlands, Poland, Sweden, and the European Union were assessed to identify synergies in recommendations for MS medicines (approved 1995-2020). A content analysis of HTA reports and CGs was performed to identify similarities and discrepancies in wording of treatment recommendations across documents. We assessed 132 HTA reports and 9 CGs for 16 MS treatments. Final recommendations for reimbursement and inclusion in CGs were mostly similar (90%), albeit with considerable differences in treatment lines and subindications. Since 2010, HTA reports refer to the use of CGs in 42% (55/132) and to consultations with clinicians in 43% (57/132) of cases. Six of nine CGs referred to HTA reports and two referred to HTA consultations, in one case having a formal relation to the HTA organization. CGs referenced pharmacoeconomic studies (4/9) for costs and cost-effectiveness. To date, not all new HTA recommendations for MS treatments are included in CGs. Some synergy exists between treatment recommendations in HTA reports and CGs, although discrepancies were seen in timelines and in recommended treatment lines and subindications. More stakeholder dialogue and/or consultation of each other's publications may further improve synergy, facilitate transparency, and enhance patient access.

OBJECTIVES: This study aims to provide an overview of the gaps and challenges in the value assessment of biosimilars and to identify potential approaches to address them.

METHODS: A multidisciplinary, international team of biosimilar experts identified gaps and challenges. A systematic review was conducted of the peer-reviewed literature in PubMed, EMBASE, Web of Science Core Collection, EBSCOhost Business Source Complete; and of the gray literature. Preliminary results were presented at ISPOR conferences and this article benefited from 2 review rounds among ISPOR Biosimilar Special Interest Group members.

RESULTS: Given that a biosimilar is highly similar to its reference biologic, health technology assessment agencies should accept the comparability exercise approved by regulatory authorities and, thus, conduct a price comparison when biosimilar reimbursement is requested for the same indication as the reference biologic. If the reference biologic is not reimbursed or is not the standard of care, a full economic evaluation of the biosimilar versus a relevant comparator needs to be conducted. To date, little consideration has been given to specific challenges, such as how biosimilar value assessment can account for the nocebo effect, potential differences between biologic-naive and biologic-experienced patients, the availability of intravenous and subcutaneous administration forms or different administration devices for the same active compound, value-added services, and the contribution of biosimilars for generating health gain at the population level.

CONCLUSIONS: There is a need to gather further insights in the methodology of value assessment for biosimilars, and health technology assessment agencies need to develop more elaborate guidance on biosimilar value assessment in specific circumstances.

In July 2019, the National Institute for Health and Care Excellence (NICE) initiated a major review of its health technology evaluation methods to update its methods guide. This update has recently concluded with the publication of its health technology evaluation manual in January 2022. This paper reports the methods and findings of the review in relation to the recommended approach to use for the measurement and valuation of health-related quality of life (HRQoL) in submissions to NICE. Issues related to (i) the methods to use when NICE's preferred measure (EQ-5D) is not appropriate or not available; (ii) adjusting health state utility values over time to account for age; (iii) measuring and valuing HRQoL in children and young people; and (iv) including carers' QoL in economic evaluations were included in this review. This commentary summarises the methods used to undertake the review, its findings, and the changes to NICE methods that were proposed based on these findings. It also outlines topics where further research is needed before definitive methods guidance can be issued. The broad proposals described here were subject to a public consultation in 2020 and a further consultation on the updated methods guidance was completed in October 2021 before the publication of the manual in January 2022.

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance-for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

The need for innovative payment models for health technologies with high upfront costs has emerged due to affordability concerns across the world. Early technology adopter countries have been experimenting with delayed payment schemes. Our objective included listing potential barriers for implementing delayed payment models and recommendations on how to address these barriers in lower income countries of Central and Eastern Europe (CEE) and the Middle East (ME). We conducted a survey, an exploratory literature review and an iterative brainstorming about potential barriers and solutions to implement delayed payment models in these two regions. A draft list of recommendations was validated in a virtual workshop with payer experts from the two regions. Eight barriers were identified in 4 areas, including transaction costs and administrative burden, payment schedule, information technology and data infrastructure, and governance. Fifteen practical recommendations were prepared to address these barriers, including recommendations that are specific to lower income countries, and recommendations that can be applied more universally, but are more crucial in countries with severe budget constraints. Conclusions of this policy research can be considered as an initial step in a multistakeholder dialogue about implementing delayed payment schemes in CEE and ME countries.

The IMI public-private partnership between the European Commission and the European Federation of Pharmaceutical Industries and Associations (EFPIA) was launched in 2008 with an initial budget of €2 billion. Aiming to accelerate the development of innovative medicines for areas of unmet clinical need, the IMI has committed over €380 million to projects on neurodegenerative disorders (NDD), catalyzing public-private collaborations at scale and at all stages of the R&D pipeline. Because of this vast investment, research on neurodegenerative diseases has made enormous strides in recent decades. The challenge for the future however remains to utilize this newly found knowledge and generated assets to develop better tools and novel therapeutic strategies. Here, we report the results of an integrated programme analysis of the IMI NDD portfolio, performed by the Neuronet Coordination and Support Action. Neuronet was launched by the IMI in 2019 to boost synergies and collaboration between projects in the IMI NDD portfolio, to increase the impact and visibility of research, and to facilitate interactions with related initiatives worldwide. Our analysis assessed the characteristics, structure and assets of the project portfolio and identifies lessons from projects spanning preclinical research to applied clinical studies and beyond. Evaluation of project parameters and network analyses of project partners revealed a complex web of 236 partnering organizations, with EFPIA partners often acting as connecting nodes across projects, and with a great diversity of academic institutions. Organizations in the UK, Germany, France and the Netherlands were highly represented in the portfolio, which has a strong focus on clinical research in Alzheimer's and Parkinson's disease in particular. Based on surveys and unstructured interviews with NDD research leaders, we identified actions to enhance collaboration between project partners, by improving the structure and definition of in-kind contributions; reducing administrative burdens; and enhancing the exploitation of outcomes from research investments by EU taxpayers and EFPIA. These recommendations could help increase the efficiency and impact of future public-private partnerships on neurodegeneration.

To conduct a situational analysis with the aim to inform future health technology assessment efforts (HTA) in Egypt. The Egyptian government has set universal health coverage as a 2030 target. Several agencies have been created in the context of the ongoing healthcare reform. The Egyptian Authority for Unified Procurement, Medical Supply and the Management of Medical Technology (UPA) is one of them and was established to support strategic procurement using HTA. Description of the development of HTA in Egypt supported by a literature search as part of a scoping exercise, and a stakeholder analysis and identification of HTA capacity survey, based on previous surveys, with relevant stakeholders conducted in 2022. This was followed by a stakeholder event where results were shared and further contextualized. The UPA is expected to evaluate the cost-effectiveness of health technologies and public health programs. The HTA process is being developed, focusing on the assessment of the value of new pharmaceuticals being introduced to the Egyptian market. A total of 16 participants responded on behalf of their organizations to the stakeholder analysis and identification of HTA capacity survey. More than 80% of the respondents were familiar with current efforts conducted by UPA and strongly support the implementation of HTA in Egypt. Transparency was highlighted as an important criterion. Over 90% of the respondents mentioned economic analyses as an HTA product being developed in Egypt, and medicines were the type of technology that stakeholders ranked as first in the rank of health technologies that need the output from HTA urgently. Capability building and training were highlighted as areas in which further support is required. This study represents the first attempt to describe the current path for HTA in Egypt. There seems to be momentum in Egypt to proceed and advance with HTA institutionalization. It would be important that next steps are built on the skills and capabilities already in place in Egypt, ensure methods and processes are in place and up to date and involve the wider system in Egypt so stakeholders can appropriately contribute and participate in the HTA process.

Outcome-based reimbursement models can effectively reduce the financial risk to health care payers in cases when there is important uncertainty or heterogeneity regarding the clinical value of health technologies. Still, health care payers in lower income countries rely mainly on financial based agreements to manage uncertainties associated with new therapies. We performed a survey, an exploratory literature review and an iterative brainstorming in parallel about potential barriers and solutions to outcome-based agreements in Central and Eastern Europe (CEE) and in the Middle East (ME). A draft list of recommendations deriving from these steps was validated in a follow-up workshop with payer experts from these regions. 20 different barriers were identified in five groups, including transaction costs and administrative burden, measurement issues, information technology and data infrastructure, governance, and perverse policy outcomes. Though implementing outcome-based reimbursement models is challenging, especially in lower income countries, those challenges can be mitigated by conducting pilot agreements and preparing for predictable barriers. Our guidance paper provides an initial step in this process. The generalizability of our recommendations can be improved by monitoring experiences from pilot reimbursement models in CEE and ME countries and continuing the multistakeholder dialogue at national levels.

Patients' perspectives are important to identify preferences, estimate values and appreciate unmet medical needs in the process of research and development and subsequent assessment of new health technologies. Patient and public involvement in health technology assessment (HTA) is essential in understanding and assessing wider implications of coverage and reimbursement decisions for patients, their relatives, caregivers, and the general population. There are two approaches to incorporating the patients' voice in HTA, preferably used in a mix. In the first one, patients, caregivers and/or their representatives directly participate at discussions in different stages of the HTA process, often at the same table with other stakeholders. Secondly, patient involvement activities can be supported by evidence on patient value and experience collected directly from patients, caregivers and/or their representatives often by patient groups Patient involvement practices, however, are limited in Central and Eastern European (CEE) countries without clear methodology or regulatory mechanisms to guide patient involvement in the HTA process. This poses the question of transferability of practices used in other countries, and might call for the development of new CEE-specific guidelines and methods. In this study we aim to map potential barriers of patient involvement in HTA in countries of the CEE region.

In the UK, 4.7 million people are currently living with diabetes. This is projected to increase to 5 million by 2025. The direct and indirect costs of T1DM and T2DM are rising, and direct costs already account for approximately 10% of the National Health Service (NHS) budget. The aim of this review is to assess the economic models used in the context of NICE's Technology Appraisals (TA) Programme of T1DM and T2DM treatments, as well as to examine their compliance with the American Diabetes Association's (ADA) guidelines on computer modelling. A review of the economic models used in NICE's TA programme of T1DM and T2DM treatments was undertaken. Relevant TAs were identified through searching the NICE website for published appraisals completed up to April 2021. The review also examined the associated Evidence Review Group (ERG) reports and Final Appraisal Documents (FAD), which are publicly accessible. ERG reports were scrutinised to identify major issues pertaining to the economic modelling. The FAD documents were then examined to assess how these issues reflected on NICE recommendations. Overall, 10 TAs pertaining to treatments of T1DM and T2DM were identified. Two TAs were excluded as they did not use economic models. Seven of the 8 included TAs related to a novel class of oral antidiabetic drugs (OADs), gliflozins, and one to continuous subcutaneous insulin infusion (CSII) devices. There is a lack of recent, robust data informing risk equations to enable the derivation of transition probabilities. Despite uncertainty surrounding its clinical relevance, bodyweight/BMI is a key driver in many T2DM-models. HbA1c's reliability as a predictor of hard outcomes is uncertain, chiefly for macrovascular complications. The external validity of T1DM is even less clear. There is an inevitable trade-off between the sophistication of models' design, their transparency and practicality. Economic models are essential tools to support decision-making in relation to market access and ascertain diabetes technologies' cost effectiveness. However, key structural and methodological issues exist. Models' shortcomings should be acknowledged and contextualised within the framework of technology appraisals. Diabetes medications and other technologies should also be subject to regular and consistent re-appraisal to inform disinvestment decisions. Artificial intelligence could potentially enhance models' transparency and practicality.

Evidence about the relative effects of new treatments is typically collected in randomised controlled trials (RCTs). In many instances, evidence from RCTs falls short of the needs of health technology assessment (HTA). For example, RCTs may not be able to capture longer-term treatment effects, or include all relevant comparators and outcomes required for HTA purposes. Information routinely collected about patients and the care they receive have been increasingly used to complement RCT evidence on treatment effects. However, such routine (or real-world) data are not collected for research purposes, so investigators have little control over the way patients are selected into the study or allocated to the different treatment groups, introducing biases for example due to selection or confounding. A promising approach to minimise common biases in non-randomised studies that use real-world data (RWD) is to apply design principles from RCTs. This approach, known as 'target trial emulation' (TTE), involves (1) developing the protocol with respect to core study design and analysis components of the hypothetical RCT that would answer the question of interest, and (2) applying this protocol to the RWD so that it mimics the data that would have been gathered for the RCT. By making the 'target trial' explicit, TTE helps avoid common design flaws and methodological pitfalls in the analysis of non-randomised studies, keeping each step transparent and accessible. It provides a coherent framework that embeds existing analytical methods to minimise confounding and helps identify potential limitations of RWD and the extent to which these affect the HTA decision. This paper provides a broad overview of TTE and discusses the opportunities and challenges of using this approach in HTA. We describe the basic principles of trial emulation, outline some areas where TTE using RWD can help complement RCT evidence in HTA, identify potential barriers to its adoption in the HTA setting and highlight some priorities for future work.

BACKGROUND: To date, health technology assessment (HTA) agencies have not been at the forefront of decision making regarding the adoption of interventions for coronavirus disease 2019 (COVID-19). Instead, policymakers have prioritised rapid action in response to the pandemic emergency, with no assessment of value for money. As COVID-19 vaccination coverage increases and healthcare systems begin to recover, HTA agencies will be expected to assess technologies for COVID-19.

OBJECTIVE: We aimed to identify the key challenges when assessing therapeutic and diagnostic technologies for COVID-19, from the perspective of HTA agencies, and identify whether there is a case for novel HTA methods and/or processes to address them.

METHODS: We used a mixed-methods approach, by conducting an online survey of HTA agencies, to collect data about the challenges faced when assessing or planning to assess diagnostic and therapeutic technologies for COVID-19. The online survey was followed by a 'roundtable' workshop of HTA agencies' representatives to discuss the results and to elaborate on their responses.

RESULTS: We received 21 completed surveys (response rate of 45%) and 11 of the respondents joined the roundtable discussion. Five themes emerged from the responses: assessing clinical effectiveness (44%), assessing cost effectiveness (19%), practical (19%), political (11%), and decision making (11%) challenges. At the roundtable, attendees elaborated on the challenges and identified two additional themes: how HTA agencies have responded to the pandemic to date, and how their role might change over time.

CONCLUSION: HTA agencies face both methodological and logistical challenges when assessing or planning to assess technologies for COVID-19. An interim best-practice HTA framework to address the key challenges would be valuable.

BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity.

METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status.

RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.

CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

BACKGROUND: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.

METHODS: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.

RESULTS: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza ( = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.

CONCLUSIONS: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.

IMPACT: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

OBJECTIVE: To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.

DESIGN AND SETTING: This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.

PARTICIPANTS: Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.

OUTCOMES: Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported.

RESULTS: We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.

CONCLUSIONS: COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

OBJECTIVE: Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.

METHODS: A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization.

RESULTS: We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%).

CONCLUSION: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

Countries around the globe have responded to pandemic preparedness and developed strategies to cope with the COVID-19 crisis. In this context, the role of healthcare professionals is of paramount importance. Pharmacists are playing a vital role in dealing, preparedness, prevention, protection, promoting access to medicines and to improve health outcomes during this crisis. In this context, "Drive-thru" pharmacy services improve access to medicines while ensuring the preventive measures suggested by the World Health Organization. This commentary provides an overview of opportunities and challenges related to the implementation of "drive-thru pharmacy services" and their role in improving public health during this crisis.