bluuebunny/crossref_metadata_2025_split huggingface dataset:
PP143 International Assessment of the Health Care System in Kazakhstan. A performance analysis.
Authors: Vilariño-López María del Carmen , Kosherbayeva Lyazzat , Zhorayev Olzhas , Zhaksylyk Madi 
Published: December 2022

IntroductionMeasuring the performance of the health systems is an important challenge at international level. The main objective of this work is to analyze the outcomes of the Kazakhstan Health Care System in order to establish the main causes of avoidable mortality in the country. Also, to identify benchmarking possibilities that may support public policy decisions to improve the results.MethodsTo calculate the avoidable mortality indicators due to preventable and treatable causes, the methodology agreed by the OECD and Eurostat based on the International Classification of Diseases, ICD-10 was applied. Starting from the mortality database of the World Health Organization, the standardized indicators of avoidable mortality was calculated for those countries that had available data based on this classification. Based on the outcomes obtained, a “Two-Step” Cluster Analysis was used to identify and characterize the different clusters of countries that present similar results to identify possible affinities and detect benchmarking possibilities.ResultsThe main causes of mortality from treatable diseases in Kazakhstan are those related to the circulatory system, followed by different types of cancer and respiratory diseases.Applying the cluster analysis in the international context, we find important differences between the different clusters, both in the standardized ratios of avoidable mortality and in its causes. Notable differences have also been identified between Kazakhstan and the countries that make up its cluster. Overall, Kazakhstan presents better avoidable mortality results, both from preventable and treatable causes, than the average of the cluster to which it belongs. However, in some causes of death, it presents worse results and high mortality rates, as in the case of those related to the circulatory and respiratory systems or different types of injuries.ConclusionsThe cluster analysis based on the avoidable mortality indicators reveals different conglomerates of countries that show important similarities between them and also some significant differences. Groups of avoidable diseases that characterize each cluster and subcluster, provide key information for the benchmarking and the design of future actions.
PP145 Improving Patient Expert Involvement In The Lifecycle Of Health Technology Assessments To Build Public Confidence In Decision-Making
Authors: Tonkinson Mandy , Livingstone Heidi , Marsden Laura 
Published: December 2022

IntroductionInvolving patients in the health technology assessment (HTA) lifecycle is a core principle at the National Institute for Health and Care Excellence (NICE). We include both patient organizations and patient experts, which helps build public confidence in healthcare decision-making. We continually work with patient experts to improve their experience and ability to participate by seeking patient expert feedback after every committee meeting.MethodsWe sent patient experts an anonymous experience survey containing a five-point Likert scale and open text boxes to capture qualitative data. The survey covered their overall experience, interaction with the committee Chair, and the support they received from both NICE and the Public Involvement Programme (PIP).In the 2019 to 2020 period we sent out 59 questionnaires and received 29 responses (47%), all of which were from medicines HTA committee participants. In the 2020 to 2021 period we sent out 120 questionnaires and received 65 responses (54%), of which 64 were from patient experts who attended medicines HTAs and one was from a medical devices HTA committee participant.ResultsGood or excellent experiences were reported by 90 percent of patient experts. The four main success factors noted were: good support before meetings; being welcomed and respected; well organized meetings; and patient expert input being valued. Areas for further improvement included: providing better briefing before meetings; allowing more time to review documents; providing more technical support; and giving more consideration to the opinions of patient experts.ConclusionsAs a result of the feedback received, the PIP now holds monthly group briefing meetings for patient experts. We also publish the anonymized feedback from the patient experts quarterly in a newsletter for committee members and share the data with internal NICE teams. Additionally, NICE aims to: send committee papers out earlier; have the option of holding a technical engagement call before committee meetings; and develop a feedback mechanism to ascertain the impact of patient input.
PP152 The Assessment Of The Price Of A Medicine: The Possible Application Of Cost-Based Pricing Methods
Authors: Van Den Berg Sibren , Canoy Marcel , Timmers Lonneke , Hollak Carla 
Published: December 2022

IntroductionBefore admission to the insured package, the price of a medicine is usually assessed on the basis of the value of the medicine for the patient: the effect size on health and survival must be in line with the costs. That seems like a fair starting point, but the use of such ‘value-driven’ models sometimes results in unrealistic prices. These prices in turn lead to discussions about limitations within the healthcare budget and may result in delays in the accessibility of medicines. The aim of this study was to review several alternative pricing models and propose possible applications of the models.MethodsSix pricing models were selected that encompassed cost-based or cost- and value-based aspects. The models were reviewed within the context of the published group of medicines, followed by a discussion on their potential to aid in creating benchmarks for pricing negotiations.ResultsFive cost-based pricing models and one value-based model with a cost-based aspect were found with potential applications. (i) The AIM-model for innovative medicines. (ii) The adjusted AIM-model for repurposed medicines. (iii) The Cancer drug pricing model for innovative oncolytics with information about health benefit. (iv) The discounted cash flow model for orphan medicines with information about health benefit. (v) The value-based rate of return pricing for innovative orphan medicines with information about health benefit. (vi) The P-quad model for drugs with a high budget impact for which there is no generic competition after intellectual property an regulatory exclusivity end.ConclusionsWe argue that it would be more logical for different categories of medicines to base prices on average costs, possibly combined with a bonus for innovation: the cost-based pricing method. The next step is to discuss the possible application of cost-plus methods with stakeholders including patients, industry, payers, and healthcare professionals.
PP153 The Economic And Fiscal Impact Of Public Health Programs For Diabetic Patients In Italy
Authors: Di Brino Eugenio , Basile Michele , Rumi Filippo , Calabrò Giovanna Elisa , Cicchetti Americo 
Published: December 2022

IntroductionTechnological innovations in the health sector have economic implications that go beyond their effects on health expenditure, expanding into other areas of the state budget (e.g., the social security system). Furthermore, innovation can affect the production of wealth by workers and companies, which in turn affects tax revenues. In addition, the presence of chronic diseases tends to reduce the propensity to consume and changes the allocation of consumption between the different sectors. Allocative decisions in the health system are rarely supported by an analysis that combines the health effects of innovations and their consequences in the economic system.MethodsThe objective of this study was to estimate the value of management programs for patients suffering from type 2 diabetes mellitus that involved different levels of use of innovative technologies and drugs. A tax impact assessment methodology was adopted in the context of chronic conditions to analyze the effect of adopting alternative management models for patients with diabetes on the broader economic system.ResultsAssuming a policy that reduces annual complications by 0.42 percent, there was an increase in tax revenue (cumulative value) of approximately EUR 28,175 and a reduction in productivity losses (cumulative value) of EUR 4,049,890. Projecting the impact on the age trend of the population up to 65 years of age with these estimates, it is possible to have an increase in tax revenue (cumulative value) equal to approximately EUR 7,050,598 and a reduction in productivity losses (cumulative value) equal to EUR 140,235,923.ConclusionsIn light of this work, providing remote patient support (telemedicine) and expanding the provision of innovative oral antidiabetic drugs to family physicians could improve care for patients with type 2 diabetes mellitus. This study provides decision makers with an immediately usable model to broaden the information base for planning and regulatory choices. In addition, it supports the use of economic evaluations that calculate the entire value of a technological innovation or health program.
PD07 Cost-Effectiveness Of Direct Oral Anticoagulants In Chinese Patients With Non-Valvular Atrial Fibrillation
Authors: Peng Kuan , Li Yihua , Li Xue 
Published: December 2022

IntroductionThe emergence of direct oral anticoagulants (DOACs) has revolutionized the prevention of stroke-related non-valvular atrial fibrillation (NVAF). Although several DOACs are available, studies comparing the cost effectiveness of DOACs with vitamin K antagonists for NVAF are scarce. The objective of this study was to assess the cost effectiveness of DOACs and warfarin from the Hong Kong public institutional perspective to inform formulary listing decisions.MethodsA previously developed Markov model was adapted to simulate the lifetime disease progression of a hypothetical cohort of 1,000 patients. Net monetary costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were computed for the following competing alternatives: warfarin, apixaban (5 mg twice daily), dabigatran 110mg or 150mg (twice daily), and rivaroxaban (20mg once daily). Model inputs were sourced from local real-world evidence, landmark trials, and comprehensive literature reviews. Probabilistic sensitivity analyses and deterministic sensitivity analyses were conducted to address study uncertainties. The willingness-to-pay threshold was set at one times the gross domestic product (GDP) per capita (USD 46,091) per QALY gained.ResultsIn base case results, all DOACs provided greater improvements in QALYs at a lower cost than warfarin. Using apixaban as the reference for comparisons among the DOACs, dabigatran 110 mg resulted in greater costs and lower QALY gains and was dominated by apixaban, whereas dabigatran 150 mg provided an incremental QALY of 0.005 at an incremental cost of USD 326, leading to an ICER of USD 67,633 per QALY. The lifetime cost associated with rivaroxaban was lower than for apixaban (-USD 151), but with lower QALY gains (-0.147), resulting in an ICER of USD 1,029 per QALY. In probabilistic sensitivity analysis, the probability of warfarin, rivaroxaban 20 mg, dabigatran 110 mg, dabigatran 150 mg, and apixaban 5 mg being cost effective out of 2,000 iterations was 0 percent, 0 percent, 29.4 percent, 33.2 percent, and 37.4 percent, respectively.ConclusionsThe results indicated that apixaban was the most cost-effective treatment in the management of NVAF, compared with other DOACs and warfarin. This conclusion was consistent under all the uncertainty test scenarios.
PD14 A New Equitable Biomedical Research And Development Model: Preliminary Findings From A Pilot Study Applying VALIDATE Value Methods
Authors: Espriu Marina , Bigorra Joan , Gallo Pedro , Sampietro-Colom Laura 
Published: December 2022

IntroductionThe public health areas without commercial value continue to be underserved, while those of high profit for industry will not be sustainable for much longer. We hypothesize that the lack of equity and efficiency in the biomedical research and development system is mainly due to a pharma-led short-term profit orientation that ignores the values of other relevant stakeholders.This pilot study reached some consensus on the principles of a co-created biomedical research and development process based on the preferred supplier (PS) model, which proposes a public health procurement system prioritizing business with companies fulfilling the “4 Share” criteria of priorities, risks and rewards, results, and outcomes to ensure that health needs are met.MethodsA constructive health technology assessment, which included VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology, was used to analyze the values and dissent of a pilot sample of ten global key informants. The methodology comprised qualitative techniques such as an online preliminary survey, in-depth semi-structured interviews, and a Delphi survey to reach a joint construction by reconstructing the stakeholders’ interpretive frames and applying an adaptation of the Richardson model to contested values.ResultsThere was consensus on combining efficiency and social justice norms by incentivizing diseases affected by market failure due to small subpopulations (e.g., rare diseases), low availability to pay, restricted use (e.g., antibiotics), and difficulty demonstrating results (e.g., Alzheimer’s disease). Stakeholders mainly agreed on the PS 4 Share principles, highlighting the need for price to be linked to impact modulated by tracked research and development costs and investments, as proposed by the PS model. More market incentives such as push, and especially pull incentives (market access), should be included. The PS model should be cause-solution oriented, promote open-disruptive innovation, and guarantee fast patient access.ConclusionsThe PS model achieved multistakeholder consensus for desirability (values) and plausibility (shifting incentives). Stakeholders pointed out the need to apply health technology assessment to further develop the model and pilot it in the European Union with the NextGenerationEU.
PD15 Horizon Scanning To Nominate Subsidy Evaluation Topics For Medical Technologies: Early Experience From Singapore
Authors: Ju Hong , Ng Zhen Long , Ling Jeffrey , See-Toh Rachel , Ng Kwong 
Published: December 2022

IntroductionHorizon scanning (HS) is an early health technology assessment (HTA) method for raising early awareness among policymakers of promising new and emerging health technologies. The Agency for Care Effectiveness (ACE) in Singapore established a HS system in 2019 to complement its HTA process. While the HS system initially focused on cell and gene therapies, this was expanded in 2020 to include medical technologies (MTs). This abstract focuses on the role of HS in nominating MTs for early evaluation to guide subsidy decisions, with the intention of avoiding challenges in altering deeply entrenched practices.MethodsThe ACE methodology for HS aligns with the core principles and methods of international best practice. Generally, MTs addressing national healthcare priorities are tracked. To identify topics for subsidy evaluation, the local registration status of an MT was used as the main selection criterion because of its proximity to the technology’s early diffusion into the healthcare system. MTs with regulatory approval were selected for HTA and subsidy consideration. All nominated technologies were checked against eligibility criteria for HTA and then assessed against a standard checklist for prioritizing HTA topics.ResultsAmong the 1,025 MTs tracked by the HS system, 89 were locally registered and nominated for HTA. Following eligibility assessment, 26 topics remained. After the prioritization exercise six topics were shortlisted. To date, two evaluations have been completed to guide subsidy decisions and four topics are undergoing evaluation. Notably, 16 of the 26 eligible topics were excluded due to a lack of sufficient evidence, in terms of both quantity and quality, for evaluation.ConclusionsHS can be a useful tool for identifying new MTs for evaluation and possible funding prior to further diffusion, but careful selection of the technologies is required to ensure a sufficient evidence base for evaluation. Moving forward, HS can also play a more active role in disinvestment of obsolete or low value health technologies.
PD16 Reassessment And Selection Strategy Of Dipeptidyl Peptidase-4 Inhibitors For Ministry Of Health Malaysia Medicine Formulary
Authors: Idrus Nazatul Syima , Low Ee Vien , Md Yusof Faridah Aryani , Mahamad Dom Siti Hajar , Abd Rahim Sarahfarina 
Published: December 2022

IntroductionThe number of anti-diabetics listed in the Ministry of Health Medicine Formulary (MoHMF) and the proposal to change prescribing category for usage in primary care facilities has increased each year causing difficulty in monitoring effectiveness, safety and rational use of the treatment and budget management. This study aimed to describe the reassessment and selection strategy of Dipeptidyl Peptidase-4 Inhibitors (DPP4-i), a class of anti-diabetic medicines in the MoHMF.MethodsA literature review on the comparative effectiveness and safety of all available DPP4-i in MoHMF were conducted. Comparative treatment cost and utilization of DPP4-i were analyzed. Approved MoHMF indications were listed and compared against approved Drug Control Authority (Malaysia) indications. Information on clinical guidelines recommendations and listing or reimbursement status in non-Ministry of Health Malaysia (MoHM) public institutions and other countries were obtained. All findings were presented to MoHM drug expert committee for DPP4-i selection strategy. The final recommendation based on consensus among drug expert committee and Pharmaceutical Services Program were presented to the MoHMF Panel.ResultsThe MoHMF Panel acknowledged that the efficacy and safety profile of all DPP4-i were equivalent across therapeutic group as supported by strong evidence, hence, their selection can be made via cost-minimization strategy. The call for contract tender for single tablet was conducted based on therapeutic group (drug class) to encourage price competition and contracts were awarded to two DPP4-i which offered the lowest treatment costs. Saxagliptin and vildagliptin were awarded as contract items while sitagliptin remained as local purchase item. Prescribing category for all DPP4-i in MoHMF were streamlined accordingly. Linagliptin was disinvested due to sufficient availability of alternatives.ConclusionsSelection strategy and disinvestment has successfully reduced the number of DPP4-i listed in MoHMF thus allowing more efficient clinical and cost monitoring. Cost minimization through tender by therapeutic group was the first to be done and has efficiently provided an avenue for price competition which results in saving to MoHM.
PD17 Automating The Impact Reporting Of NICE Guidance
Authors: Rowark Shaun , Jonsson Pall , Kent Seamus 
Published: December 2022

IntroductionThe National Institute for Health and Care Excellence (NICE) intends to automate the way it monitors the uptake, impact, and value of its guidance. Traditionally this has been done by developing impact reports, long documents that, while well received, are time consuming to develop and can quickly become outdated.MethodsWe focused on a novel topic that would benefit from new data sources to examine its impact: a rapid guideline for managing the long-term effects of coronavirus disease 2019 (COVID-19). We shortlisted “measurable” recommendations within the guideline that were likely to be captured in data collections. We then reviewed available data sources that included relevant up-to-date data. Finally, we explored what existing methods were available to NICE for automating impact reporting.ResultsFor long COVID-19 we accessed OpenSAFELY, a secure, transparent software platform for primary care COVID-19 data that was developed in response to the pandemic. This captured data on the management of long COVID-19 in primary care as well as onward referral to specialist clinics. In addition, we accessed data from the CVD-COVID-UK/COVID-IMPACT Consortium, which links general practice records with primary care dispensing data. This enabled us to analyze the impact of the pandemic on the prescribing and dispensing of cardiovascular disease medications. Working with our digital team we developed an automated impact reporting dashboard using Google’s data studio. This enabled different views of the data, for example by region or socioeconomic status, to be presented in an automated way.ConclusionsAutomating the impact reporting of NICE guidance provides up-to-date information on its value to the health system. While we were able to collect new sources of data and automate some aspects of how these were viewed, full automation requires several enablers. These include an application programming interface between the data sources and NICE, and ensuring that NICE guidance is computer readable so that its measurement is practical in healthcare systems.
PD19 Machine Learning Modelling For Clinical Trial Design Using The National Institute for Health and Care Research Innovation Observatory’s ScanMedicine Database
Authors: Kavalci Ece , Sadek Jawad , Young Michael , Marshall Christopher 
Published: December 2022

IntroductionClinical trials that fail prematurely due to poor design are a waste of resources and deprives us of data for evaluating potentially effective interventions. This study used machine learning modelling to predict clinical trials’ success or failure and to understand feature contributions driving this result. Features to power the modelling were engineered using data collected from the National Institute for Health and Care Research Innovation Observatory’s ScanMedicine database.MethodsUsing ScanMedicine, a large dataset containing 641,079 clinical trial records from 11 global clinical trial registries, was extracted. Sixteen features were generated from the data based on fields relating to trial design and eligibility. Trials were labeled positive if they were completed (or target recruitment was achieved) or negative if terminated/withdrawn (or target recruitment was not achieved). To achieve optimal performance, phase-specific datasets were generated, and we focused on a subsample of Phase 2 trials (n=70,167). Ensemble models using bagging and boosting algorithms, including balanced random forest and extreme gradient boosting classifiers were used for training and evaluating predictive performance. Shapley Additive Explanations was used to explain the output of the best performing model and calculate feature contributions for individual studies.ResultsWe achieved a weighted F1-score of 0.88, Receiver Operator Characteristic Area under the Curve score of 0.75, and balanced accuracy of 0.75 on the test set with the xgBoost model. This result shows that the model can successfully distinguish between classes to predict if a trial will succeed or fail and subsequently output the features driving this outcome. The number of primary outcomes, whether the study was randomized, target sample size and number of exclusion criteria were the most important features affecting the model’s prediction.ConclusionsThis study is the first to use predictive modelling on a large sample of clinical trial data obtained from 11 international trial registries. The prediction outcomes achieved by our novel approach, which uses phase-specific trained models, outperforms previous modelling in this space.