The Chinese National Reimbursement Drug List (NRDL) was established in the early 2000’s and includes the drugs both fully and partially covered by National Basic Medicine Insurance. As China’s health system has been reformed over the past decade, it is important for manufacturers to understand the ever-changing reimbursement process and its implications on newly launched drugs. This study provides an updated overview of the process based on research conducted in 2021.

Targeted secondary research was undertaken to evaluate the pricing and reimbursement landscape in China. Primary research was conducted to assess the perspectives of three payers and one policy expert.

National listing remains the most viable and exclusive pathway to get a product reimbursed by public health insurance in China. Since 2017, the NRDL has been updated annually, and revisions are managed by the National Healthcare Security Administration (NHSA). Insights from 2021 suggests that the process of listing a new product on the NRDL lasts five months (July to November). Manufacturers should ensure that submissions are made when the annual NRDL process formally begins, and clinical and health economic evidence is compulsory. If a successful opinion is made by the assessment board, the manufacturer will be invited to negotiate a price with the NHSA. Data from the NHSA indicated that a total of 704 applications were made in 2020. In addition, 138 exclusive drugs were eligible for price negotiation, of which 96 drugs were successful and added to the NRDL. Findings also suggested that the average discount rate increased from 44.0 percent in 2017 to 50.6 percent in 2020.

The national reimbursement process in China has become more transparent overtime. Even so, NRDL listing remains a challenge, with decisions driven by clinical and pharmacoeconomic evidence, and price. Significant price cuts should be considered and anticipated to ensure successful negotiation outcomes.

Patients with spinal deformity require implanted spinal rods to be specifically shaped to their anatomy. Spinal rods which are manually shaped in the operating theatre are prone to fracture and malpositioning. This emphasizes the need for preoperative planning, intraoperative imaging, and accurate shaping of the implant. The application of machine learning (ML) has enabled precision in shaping patient-specific rods, with the potential to reduce adverse events. Our objective is to assess the economic value of this technology, early in its lifecycle, in the context of Health Technology Assessment (HTA) in Australia.

A budget impact analysis was performed to quantify the economic value of patient-specific spinal rods from an Australian payor perspective. Clinical outcomes were sourced from literature review, and cost inputs were obtained from Medicare, Private Health Data Bureau and Hospital Casemix Protocol Data databases.

Preliminary analysis indicates that a reduction in the rate of revision surgery due to decreased instrument failure results in cost-savings to the healthcare system, despite a higher outlay for the patient-specific rods. Adolescents who may have remained sagittally malaligned after the implantation of manually bent rods are expected to derive the greatest benefit from this ML application. The key uncertainty in this analysis is the limited real-world data of this emerging technology. ML is an iterative process of continuous improvement, identifying correlations within the data collected. As additional surgical data are integrated into predictive models, we anticipate ML technology will enhance decision-making support in surgical strategy and enable better implant precision, resulting in further decreased operating time, reduced mechanical complications, and increased healthcare savings.

ML technology is enabling precision in patient-specific implants, which is expected to drive healthcare cost-savings due to a reduction in instrument failure. Fewer replacement surgeries are an important patient-relevant outcome, especially for adolescents with spinal deformity. This preliminary analysis demonstrates the economic value of ML enabled patient-specific rods to Australian payors, early in its lifecycle.

A disruptive technology has been defined as an innovation that completely changes the way things are done. Early identification of potential disruptive health technologies has become a key point in the agenda of decision makers and health technology assessment (HTA) bodies. The PriTec tool is an automatically executable web application that was developed in 2009 by the Galician HTA Agency to facilitate decision-making regarding the selection of technologies for post-launch observation. The tool has been updated to allow scoring and ranking of technologies before their introduction into the healthcare portfolio. The aim of this work was to propose a framework for assessing the usefulness of the PriTec tool in relation to identifying possible disruptive innovations.

To evaluate the applicability of the PriTec tool for distinguishing disruptive from non-disruptive innovations, we selected a few examples from prior acknowledged disruptive and non-disruptive innovations. These technologies were scored against the predefined criteria and the results were compared and analyzed globally and by type of domain. The PriTec tool assesses six domains of technologies: clinical condition, comparative effectiveness and safety, economic impact, implementation consequences, and dissemination speed.

Disruptive technologies (e.g., transcatheter aortic valve implantation or point-of-care tests) had higher weighted global scores than non-disruptive technologies. In the domain analysis, the scores for implementation consequences were higher for disruptive than for non-disruptive technologies. Both types of technologies had similar scores in the other domains.

The PriTec tool seems to be useful for identifying potential disruptive technologies through its implementation domain. Further validation strategies are required to confirm the tool’s applicability and to improve its accuracy in the field of health disruption. The tool could be used by governments, horizon scanning organizations, and HTA organizations to promote the evidence-based detection of disruptive technologies in clinical practice. However, it is advisable that the tool be further tested and validated in other contexts.

The National Institute for Health and Care Research (NIHR) Innovation Observatory (IO) national horizon scanning research centre, has a remit to notify its stakeholders, including the National Institute for Health and Care Excellence (NICE), about innovative interventions; including biosimilar medicines in the pipeline. Biosimilar medicines bypass many developmental steps, making them substantially cheaper to manufacture for providers, which increases market availability and improves treatment access for patients.

Since 2017, the NIHR IO has monitored biosimilars in clinical development that align to the NICE health technology assessment remit. The data set explored was exported from our internal medicines innovation database - MInD.

Data sets were created that included information on the characteristics of biosimilars and their associated clinical trials. Analyses and visualization creation were carried out using Microsoft Excel and Microsoft Power BI.

A total of 100 unique biosimilar medicines in 136 clinical trials were included in the MInD since April 2017. Of these, 44 percent of biosimilars are currently EMA-approved (Nov 2021). Adalimumab was the reference medicine with the most unique biosimilars identified (12%). Seventy-two percent of the biosimilars in MInD were indicated for non-oncology conditions, twenty percent for oncology condition and eight percent for both.

There were 46 biosimilars unapproved, which were in active development. Of these biosimilars 17.4 percent are indicated for an oncology condition, 78.3 percent for non-oncology conditions, and 4.3 percent for biosimilars for both. Aflibercept was the reference product with the most (eight) biosimilars in active development.

There were 56 individual clinical trials in the MInD that list a biosimilar in development. For 26 trials, the primary completion date (PCD) was prior to 2021, whilst 28 trials listed a PCD post-2021, and 2 PCD’s were unavailable

Our analysis identified high levels of active clinical development for biosimilars. The majority of biosimilars being developed are indicated for non-oncology conditions, with many in trials due to readout in the near future. Early identification, monitoring and reporting of biosimilars allows for expedited patient access and benefits, including cost-savings for health services.

This study is funded by the National Institute for Health Research (NIHR) [(HSRIC-2016-10009)/Innovation Observatory].

Since May 2020, reimbursement can be requested for Digital healthcare applications (DIGAs) in Germany. The prerequisite for reimbursement is a listing in the Federal Institute for Drugs and Medical Devices (BfArM) DiGA-directory, granted after successful completion of an assessment process. In June 2021, IQVIA first evaluated the BfArM’s decision-making and identified criteria that may positively impact a directory listing. In November 2021, the research was updated to consider the latest developments.

Published information for each app in the DiGA-directory was qualitatively compared according to pre-specified criteria by two independent reviewers. With no data available for denied apps, only DiGAs with permanent or preliminary listings were compared.

By 26 November 2021, twenty-four apps had received a positive assessment. Permanently listed apps (n=6) focused on health status improvements and/or patients’ health competency. None claimed to improve patients’ quality of life. Results from at least one randomized controlled trial (RCT) showed efficacy with medium or strong significant effect sizes (Cohen’s d > 0.4) versus standard of care (SOC). For preliminarily listed DiGAs (n=18), final results of positive care effects were not yet provided, but applications included study designs for RCTs including definitions of primary endpoints. The BfArM accepted the trial designs to be suitable to measure an app’s positive effects on health care within twelve months after listing. Since the last analysis, no preliminarily listed apps have been granted permanent DiGA-status. Evaluation periods were extended by up to five months for three apps. With none of the trials for preliminary apps being completed, possibilities for assessment remain limited. Claims for either medical benefit and/or patient-relevant structural/procedural improvements do not seem to impact the type of listing.

Updated research confirmed previous findings. For a DiGA-directory listing DiGA, the (planned) provision of RCT data seems to be gold standard. Medium to strong effects on the improvement of health status compared to SOC appear to be important for a permanent listing. Evaluation periods of five preliminarily listed apps are expected to be completed in December 2021, allowing for further analyses in the future.

Autogenic training consists of reaching a state of deep relaxation through mental representations of physical sensations in different parts of the body. It is a promising technique for improving the psychological well-being of people with chronic diseases, but there are no clinical practice guidelines recommending the use of autogenic training in this population. The aim of this work was to identify, critically evaluate, and synthesize the available evidence on the safety and effectiveness of autogenic training in the prevention and treatment of medical conditions.

We conducted a systematic search for systematic reviews and randomized controlled trials (RCTs) in MEDLINE, Embase, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials. The selection and assessment of risk of bias of the included studies was carried out independently by two reviewers.

A total of 2,420 references were identified after eliminating duplicates. Of these, 141 were selected for reading the full text, and 13 systematic reviews and 18 RCTs were included. Compared with no intervention or delayed treatment, autogenic training significantly reduced levels of anxiety and depression as well as some physical symptoms (e.g., headaches and atopic dermatitis).

Autogenic training could be useful for improving the psychological well-being and physical symptoms of patients with chronic health conditions and as an additional element in multicomponent intervention programs. However, these conclusions are not definitive due to the low number of studies available for each health condition and their high or unclear risk of bias.

Aromatherapy is the field of herbal medicine that uses essential oils distilled from flowers, roots, and herbs and other plant compounds to promote physical and psychological well-being. Essential oils are absorbed into the body in different ways, with the inhaled and topical routes being the most widely used. The aim of this review was to critically evaluate and synthesize the available scientific evidence on the efficacy and safety of aromatherapy for the management of any therapeutic indication. This report was requested by the Spanish Ministries of Health and Science and Innovation.

An overview of systematic reviews (SRs) was performed. The MEDLINE, Embase, CINAHL, and PsycINFO databases were searched for literature published from January 2006 to August 2021. SRs reporting the efficacy and safety of aromatherapy were included. We applied no restrictions in terms of administration route or essential oil used. Two reviewers independently performed screening and selection, data extraction, and quality assessment.

We included 74 SRs covering a wide variety of populations and settings. The most reported outcome was anxiety, followed by pain, and the most commonly used essential oil was lavender. Fifteen SRs reported mild adverse events with aromatherapy. Only 11 SRs assessed the certainty of evidence using the GRADE approach. Aromatherapy reduced heart rate and likely reduces anxiety and breathing rate in patients with cardiovascular diseases. Aromatherapy probably also reduces pain in women with primary dysmenorrhea. Additionally, it may reduce blood pressure, acute pain, subjective stress, and the need for antiemetic drugs after surgical procedures. However, the evidence was very uncertain regarding the effect of essential oils on anxiety, pain, and quality of life in patients with cancer, anxiety and pain after a caesarean section, and dental anxiety.

Aromatherapy may be useful for managing psychological and physical symptoms in different settings. However, the conclusions of this review are not definitive because of the moderate to high risk of bias in many of the primary studies included in the SRs.

Health technology assessment (HTA) bodies worldwide recognize the importance of real-world evidence (RWE) in addressing uncertainties around the effectiveness of new drugs at the time of launch and as part of resubmissions. We assessed the use and acceptability of RWE by analyzing HTA recommendations.

We analyzed 24,841 HTA reports, including original submissions, resubmissions, extensions of original indications, and renewals, published from January 2011 to October 2021 from more than 100 HTA agencies across 37 countries.

Our analysis showed that 3,820 (15%) reports mentioned RWE. Between 2011 and 2021 there was an eight-fold increase in the use of RWE, from 4 percent in 2011 to 34 percent in 2021. RWE was most commonly included in HTAs in oncology (26%) and endocrine and metabolic diseases (13%). The main areas supported were effectiveness (40%), safety (38%), and epidemiology (35%). RWE supplemented evidence on survival and quality of life as well as resource utilization, proxy comparators, and utility. Based on an analysis of the 1,474 reports that mentioned RWE, effectiveness was mainly supported by cohort (22%) and observational studies (13%), safety was mainly derived from pharmacovigilance data (9%), and epidemiology data were collected from registries (23%). The top five HTA bodies mentioning RWE in their reports were from France, Germany, Poland, and the United Kingdom (n=2). RWE was most accepted when it supported safety and epidemiological considerations, and to a lesser extent when it was used for effectiveness aspects or understanding management pathways.

The inclusion and acceptability of RWE in HTA recommendations varies between HTAs according to their data requirements and assessment methods. While it is not always specified how RWE was considered, there is a clear tendency for its increased use and acceptability, albeit not in all areas. Greater use of and transparency around RWE are likely to continue as multiple RWE initiatives emerge globally.

Health Technology Assessment (HTA) agencies have recognized the importance of real-world evidence (RWE) to inform access decision-making and different HTA agencies establish distinct requirements for their local jurisdictions. The objective of this study is to understand the differences of RWE included in HTA reports and HTA agencies’ perception of RWE.

HTA reports from agencies in France, Germany, Spain, Italy, United Kingdom (UK), Canada, Australia and South Korea from January 2011 to November 2021, including original submissions, resubmissions, extensions of original indications and renewals were analyzed.

Across the eight countries, RWE has been used in nineteen percent of all HTA reports (N=2,960/15,561), with an exponential increase observed between 2019 and 2021. RWE on clinical effectiveness was mostly used in HTA submissions in the UK (twenty-two percent), with twenty-six percent perceived with full acceptance. In contrast, RWE on safety and epidemiology was reported widely in HTA reports in France and Germany (83% and 87%), respectively. Ninety-three percent of RWE received full acceptance in France, followed by forty-four percent in Germany. A mixed picture of the types of RWE included in HTA reports was observed in the other countries, with high variance of acceptance (between 5 to 37%).

France, Germany, and the UK are the top three countries with a large proportion of HTA reports where RWE was mentioned. The type of RWE used is related to a large extent to the local evidence requirements. For example, RWE around epidemiology was included widely in Germany due to the needs of providing local data for budget impact analyses required by the Federal Joint Committee (G-BA); RWE on tolerability as reported in periodic safety update reports (PSURs) needs to be included in French HTA submissions. RWE on clinical effectiveness has been evaluated the most by the UK HTA bodies.

Outpatient Parenteral Antibiotic Therapy (OPAT) is a complex medical treatment used to treat patients with severe infections. OPAT is provisioned outside hospitals. There is wide variation in the use and organization of OPAT in Denmark. OPAT is increasingly used in Danish regions and municipalities, however, there is limited knowledge on the clinical, economic and organizational consequences of this technology. The purpose of the project was to establish an evidence base for decision-making prior to any further prioritization of OPAT as an alternative to intravenous antibiotic treatment in the hospital (IPAT). The HTA was produced at the request of the Health Directors in the Danish Regions to examine the consequences of using OPAT compared with IPAT.

The results were based on a systematic literature review and qualitative interviews with leaders (n=5), administrative employees (n=5) and health professionals (n=13) involved in the delivery of OPAT. Furthermore, a micro-costing analysis based on interviews with clinical experts was conducted.

The use of OPAT led to similar or better clinical results when compared with the use of IPAT. Current evidence supports OPAT as a safe model for intravenous antibiotic treatment. The organization of OPAT varied in Denmark as well as internationally. The selection of suitable patients for the different OPAT models was crucial for a successful treatment. Insight into patients’ understanding of the pros and cons of the technology indicated that most patients preferred treatment at home. In a Danish context the microeconomic analysis showed that different OPAT models generally led to a reduction in costs compared with IPAT.

The project contributes to practice and political decision making by identifying challenges and opportunities associated with OPAT. There is no one-size-fits-all solution. The choice of OPAT model must be based on careful clinical considerations. Coordination and communication across municipalities and hospitals is challenging. Reducing organizational complexity is necessary to achieve a more standardized practice.

Modeling is important for guiding policy during epidemics. The objective of this work was to describe the experience of structuring a multidisciplinary collaborative network in Brazil for modeling coronavirus disease 2019 (COVID-19) to support decision-making throughout the pandemic.

Responding to a national call in June 2020 for proposals on COVID-19 mitigation projects, we established a team of investigators from public universities located in various regions throughout Brazil. The team’s main objective was to model severe acute respiratory syndrome coronavirus 2 transmission dynamics in various demographic and epidemiologic settings in Brazil using different types of models and mitigation interventions. The modeling results aimed to provide information to support policy making. This descriptive study outlines the processes, products, challenges, and lessons learned from this innovative experience.

The network included 18 researchers (epidemiologists, infectious diseases experts, statisticians, and modelers) from various backgrounds, including ecology, geography, physics, and mathematics. The criteria for joining the network were having a communication channel with public health decision-makers and being involved in generating evidence for public policy. During a 24-month period, the following sub-projects were established: (i) development of a susceptible-exposed-infected-recovered-like, individual-based meta-population and Markov chain model; (ii) projection of COVID-19 transmission and impact over time with respect to cases, hospitalizations, and deaths; (iii) assessment of the impact of non-pharmacological interventions for COVID-19; (iv) evaluation of the impact of reopening schools; and (v) determining optimal strategies for COVID-19 vaccination. In addition, we mapped existing COVID-19 modeling groups nationwide and conducted a systematic review of relevant published research literature from Brazil.

Infectious disease modeling for guiding public health policy requires interaction between epidemiologists, public health specialists, and modelers. Communicating modeling results in a non-academic format is an additional challenge, so close interaction with policy makers is essential to ensure that the information is useful. Establishing a network of modeling groups will be useful for future disease outbreaks.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), is a single-strand ribonucleic acid virus that was first identified in January 2020 in patients with viral pneumonia in Wuhan, China. The virus has since spread rapidly around the world, leading the World Health Organization to declare it a pandemic on 11 March 2020. In Brazil there have been 21.8 million cases of SARS-CoV-2 infection and 608,500 deaths. The objective of this study was to evaluate the cost utility of the Oxford, CoronaVac, and Janssen vaccines from the perspective of the Brazilian public health system.

Three microsimulation models were constructed using individual data. The simulations contained seven transition states related to the natural history of COVID-19. The model with a daily cycle had a time horizon of one year and used data from 289 days of the pandemic. The analysis considered direct medical costs from the Brazilian health system perspective. Outpatient, hospital, and mortality databases were used for the model inputs and patient data were stratified by age. Effective vaccines reduced the likelihood of patients becoming ill. Information on the quality of life of patients receiving treatment in the outpatient or hospital setting and disease sequelae were extracted from the published literature. The main outcome of the analysis was quality-adjusted life-years (QALYs).

The vaccines had incremental cost-utility ratios ranging from USD 4,121 (Oxford) to USD 3,160 per QALY (CoronaVac). The older the population, the lower the incremental cost-utility ratio. Given a willingness-to-pay threshold of BRL 3,129 per QALY, all the vaccines were considered cost effective in the probabilistic sensitivity analysis. The incremental cost-effectiveness ratio stratified by age ranged from USD 6,327 per QALY in patients older than 75 years (Janssen) to USD 20,993 per QALY in patients younger than 59 years (CoronaVac).

The results of this analysis, stratified by patient age, can help in the preparation of a vaccination prioritization plan.

Health technology assessment (HTA) reports are complex technical documents that address multiple aspects of the incorporation of a technology into the health care system applying complicated methodologies coming from different disciplines. The purpose of HTA is to support decision-makers and these should have an adequate level of training to fully understand these assessments. However, most HTA education programs and courses are intended for HTA doers and there is a lack of practical guidance training aimed at preparing health managers or policy makers in HTA. The objective is to describe an HTA training program developed for decision-makers of the three levels (health care administration, hospital management and clinical practice).

Rolling Collaborative Review (RCR) 01 of convalescent plasma was identified and selected because it complied with our Population Intervention Comparator Outcome Design Question. The EUnetHTA HTA adaptation Toolkit was used to check the relevance (about research question); reliability (quality of the report) and transferability (application of information to the target setting). Additional considerations regarding the local context were examined. A panel of four professionals and one patient was formed to rate the importance of the outcomes and to carry out the external review

According to the toolkit, information on RCR01 Convalescent Plasma could be adopted for the safety and effectiveness domains. The technical characteristics and current use domains were adapted and extended. It was considered of interest to include the domains of organization and ethics. The organizational aspects were answered through the information retrieved in a search for systematic reviews and guides, and with the collaboration of experts. The ethics domain was answered through a specific literature search on ethical issues related to COVID-19 and transfusions.

The use of the EUnetHTA Toolkit has been helpful in supporting the adaptation process. The adoption of the effectiveness and safety domains from already developed HTA assessments is an efficient way to provide useful information for the decision-making process. However, contextual elements should be included in the adaptation process to ensure a complete framework for the decision.

Outcomes Based Agreements (OBAs) are financial arrangements that offer the opportunity to align payment to health outcomes in the real-world, and share the financial risk by providing long-term solutions that grant access to medicines, with reimbursement only when performance is achieved. OBAs are most likely to be useful when there is high uncertainty in the clinical data, but they are difficult to design and implement, and other financial options are usually preferred by payers. As a result, OBAs have been more the exception than the norm, and there is not a clear pattern that indicates if an OBA is likely to succeed in practice.

Through a retrospective OBA exercise with NHS Wales (Project IDEATE: Innovation in Data to Evolve Agreements That Enhance patient health outcomes), we have explored the circumstances under which an OBA might be most appealing to payers, and assessed implementation challenges and solutions, to propose a framework to evaluate the feasibility of a medicine for an OBA.

Along with mitigating some of the clinical uncertainties associated with a lack of mature data at the time of launch, an OBA must also consider other factors: the commercial viability of the agreement, the associated administrative burden, and its cost of implementation. Also, the Health System commitment to a Value-Based Healthcare agenda and, most importantly, its willingness to offer long-term sustainable solutions to optimise treatment, are key to support this approach.

Practical considerations include: how the relevant outcomes are going to be selected and tracked in the real-world, how the whole model is going to fit within the current procurement and finance infrastructures, and how industry works in collaboration with the Health System.

Insights from Project IDEATE will be used to explore how our OBA feasibility framework might be applied in the future.

The integrated patient-centered, evidence-based approach to care recognizes the role of patient preferences. A discrete choice experiment (DCE) was developed with the aim of identifying the preferences of men with prostate cancer in Italy regarding the different risk-benefit factors of various treatment options.

The DCE was developed with the support of prostate cancer patients and oncologists and was based on a targeted scoping review. The final DCE included 26 choice sets divided into two blocks. The first block focused on all prostate cancer patients (both metastatic and non-metastatic), while the second block aimed to assess preferences for patients with metastatic hormone-sensitive prostate cancer (mHSPC). Patients were asked to choose from ten attributes in the first block and six in the second block. The aim was to identify attributes and levels with a statistically significant impact on patient preferences. Preference estimates were calculated using a conditional logit regression model and the results were stratified by cancer stage (metastatic or non-metastatic) in the first block.

A total of 202 patients (mean age 72 years) completed the DCE. In the first block, the most important attribute was quality of life (QoL), particularly for patients with metastatic cancer. The other three attributes found to be significant, in order of relevance to patients, were the risks of experiencing cognitive impairment, hematologic complications, and fatigue. For patients with mHSPC, QoL was the strongest determinant of preference. The risk of experiencing fatigue was also a relevant attribute, followed by skin irritation.

This study shows that the effect of treatment on QoL was the most important attribute for patients diagnosed with prostate cancer. Specific risk factors play a different role in the choice of treatment depending on cancer type, with the risk of experiencing fatigue being valued by all groups. Identifying and understanding patients’ preferences related to treatments for prostate cancer will help physicians identify the best treatment strategy.

It is estimated that approximately 1.1 million cases of prostate cancer (PCa) are diagnosed in the world every year. In general, PCa is a slow-onset cancer and less than 10 percent of cases are detected in the metastatic phase. In order to identify patients at risk of suffering from clinically significant prostate cancer (csPCa), as well as to avoid unnecessary biopsies, overdiagnosis and overtreatment, a variety of molecular biomarker detection tests have been developed.

We undertook a systematic review with meta-analyses on the effectiveness of diagnostic tests based on biomarkers in blood or urine samples for the identification of men at risk of csPCa. A cost-effectiveness analysis was conducted using a decision tree model for the short term and a Markov model for the long term, both from the social and the National Health System perspectives. The effectiveness measure was quality-adjusted life years (QALYs). We ran extensive sensitivity analyses, including a probabilistic sensitivity analysis.

Sixty-five studies were included with a total of 34,287 participants. The diagnostic tests analyzed were: PHI, Progensa® PCA3, SelectMDx, MyProstateScore, 4Kscore®, TMPRSS2: ERG, Stockholm3, ExoDx Prostate IntelliScore and Proclarix®. All studies included biopsy as comparator. The sensitivity and specificity of diagnostic tests depended on the test itself and the threshold chosen, and ranged from 42 percent to 99 percent and from 13 percent to 87 percent, respectively. In the cost-effectiveness analysis, the alternative that includes the biomarker, specifically the SelectMDx, led to higher QALYs and healthcare costs with an estimated incremental cost-effectiveness ratio (ICER) of 6,640.21 EUR per QALY. The sensitivity analyses confirmed that the results were robust.

Biomarker testing to select men at risk of csPCa who should undergo prostate biopsy can be a cost-effective strategy depending on its cost per determination and its sensitivity/specificity. The analyses carried out indicate that the SelectMDx biomarker is cost-effective at a cost of EUR 375 per determination.

Next-generation sequencing (NGS) can be run in-house or outsourced to an independent laboratory. It has enabled wider use of deoxyribonucleic acid/ribonucleic acid (DNA + RNA) sequencing in clinical practice. Within oncology, NGS has paved the way for more effective treatment, including personalized medicine. There are, however, large variations in access and reimbursement across Europe. The aim is to understand the European NGS landscape and barriers to access.

Structured telephone interviews covered topics on NGS perception, guidelines, use-cases, benefits, costs, and future expectations. Twelve experts per country (France, Germany, Italy, Spain): two payers, five oncologists, and five pathologists were interviewed between June and August 2021. Responses were translated into English for qualitative analysis.

NGS was considered most useful when there were approved, targeted treatments. Although often noted that there was a lack of published evidence to support a beneficial link, respondents perceived that NGS has the potential to improve patient quality of life (QoL) and reduce resource use through avoiding suboptimal treatment. All of the payer respondents expected the role of NGS to increase, though it may be held back by lack of reimbursement. Respondents favored in-house NGS over outsourcing in terms of clinical benefit: “Advantages of in-house NGS are turnaround time, results and lean processes” … “you build the expertise in-house. If you have urgent samples, it’s easy to prioritize them”. Reasons for not having in-house NGS included “costs, lack of personnel. Basically, organizational and financial issues.” In-house NGS was perceived to be associated with high setup-costs (acquisition, setup, training), but lower running costs (per-test costs). There was a view that in-house solutions are mainly suitable in larger centers undertaking many tests.

NGS can save costs and provide QoL benefit through enabling optimized, personalized therapy, but published evidence establishing the outcomes link is lacking. From the hospital perspective, investing in NGS requires understanding the cost of NGS over its entire lifecycle, likely entailing a health-technology assessment including health-economic analysis.

Pelvic floor diseases (PFD) often affect the adult population, with approximately 50 percent of women aged 50 years and older suffering from PFD. The condition has complex symptoms that may lead to stress and a negative impact on the quality of life. The USA spends about USD 1 billion annually on inpatient costs as a result of more than 700,000 emergency department visits and 50,000 hospitalizations associated with PFD. Defecography is currently the most commonly used method of diagnostic imaging used to evaluate PFD. This technology is not available in the Republic of Kazakhstan, despite its high diagnostic value.

In order to assess the clinical effectiveness of defecography, we have conducted a literature search in the MEDLINE database. We selected articles with pairwise comparisons of defecography with other tests: anorectal manometry; balloon expulsion test; electromyography; magnetic resonance imaging (MRI); and, ultrasound.

The findings are primarily based on two meta-analysis (91 studies representing 10,768 cases). The results have been evaluated according to the detection frequency and omission of the following signs: cystocele, middle compartment descent, rectocele, intussusception, rectal prolapse, enterocele, and perineal descent. The literature review did not reveal significant difference in diagnostic value between MR defecography and defecography based on X-ray. However, the imaging endpoints (detection rate of structural and functional abnormalities) for defecography based on X-ray were significantly better than plain radiography.

The defecography is currently considered the gold standard and the most commonly used tool of diagnosis. The implementation does not require an expensive equipment purchase (MR defecography) and is reproducible on conventional X-ray equipment if the necessary inventory is available.

Osteoarthritis (OA) is a heterogeneous group of diseases of various etiologies based on the defeat of all components of the joint. OA is one of the main causes of disability in older people. To date, joint replacement is the most clinical and cost-effective method of the terminal stage treatment. The short and long-term success of total joint replacement is closely related to the accuracy of the prosthesis implantation. Published studies show that the accuracy of prosthesis implantation can be intraoperatively controlled by computed navigation and robotic systems better than by traditional methods.

In order to assess the clinical effectiveness of the technologies, we have conducted a literature search in the MEDLINE database. We included studies that reported a comparison of outcomes between conventional methods, computer navigation and robot-assisted surgery.

The results of this literature review are based on six systematic reviews with meta-analyses (101 studies representing 482,367 cases) and one national joint replacement registry. The outcomes compared included Knee Society Score (KSS)-function, alignment correction, mechanical axis (varus and vagus deviance >3°), prosthesis positioning, soft tissues balancing and functional outcomes. Thus, the cumulative success rate in the computed navigation and robotic systems group was reported to be 86.7 percent, which is crucial considering its lowered rate of revision (3%), correct mechanical axis (≤3%) and functional status.

The literature review demonstrates a high potential of the computed navigation and robotics systems in the intraoperative monitoring of important surgical parameters for achieving the best intervention outcomes. All the clinical endpoints were significantly better by comparison with conventional method.

Risk prediction models, using either machine learning or statistical algorithms, can act as inputs of a cost-effectiveness model when predicting costs and effectiveness of an intervention. This systematic review has two objectives: to evaluate methodological quality of the published models to predict diabetic coronary heart disease (CHD) risk; to evaluate whether the models were sufficiently reported to judge their applicability to the cost-effectiveness modelling.

A targeted review of journal articles published in English, Dutch, Chinese, or Spanish was undertaken in PubMed, Embase, Scopus, Web of Science, and IEEE Explore from 1 January, 2016 to 31 May, 2021. To assess the methodological quality and reporting of the models, we used PROBAST (Prediction model Risk Of Bias Assessment Tool), CHARMS (a Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies), and a checklist (Betts 2019) summarizing the application of cardiovascular risk prediction models to health technology assessment.

Our search retrieved 6,579 hits, of which 18 models were eligible for inclusion. Among them, four studies developed machine learning models (2 recurrent neural networks, 1 random forest models, and 1 multi-task learning model) while 14 studies developed statistical models (8 Cox models, 5 logistic models, and 1 microsimulation model). More than 70 percent of models were of high methodological quality in aspects of participants (89%), predictors (72%), and outcomes (72%), while only five models (28%) in aspects of statistical analysis. For the reporting, only two models provided sufficient evidence in all aspects (i.e., participants, predictors, and outcomes) for judging their applicability to the cost-effectiveness modelling. Most models were reported sufficiently regarding participants (78%) and outcomes (72%), but only three models regarding predictors (17%).

To apply the CHD risk prediction models to cost-effectiveness modelling, concerns remain regarding the potential risk of bias due to inappropriate use of analysis methods, and regarding insufficient reporting on how to measure and assess the predictors.