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BackgroundHealth technology assessment (HTA) is increasingly performed at the local or hospital level where the costs, impacts, and benefits of health technologies can be directly assessed. Although local/hospital-based HTA has been implemented for more than two decades in some jurisdictions, little is known about its effects and impact on hospital budget, clinical practices, and patient outcomes. We conducted a mixed-methods systematic review that aimed to synthesize current evidence regarding the effects and impact of local/hospital-based HTA.MethodsWe identified articles through PubMed and Embase and by citation tracking of included studies. We selected qualitative, quantitative, or mixed-methods studies with empirical data about the effects or impact of local/hospital-based HTA on decision-making, budget, or perceptions of stakeholders. We extracted the following information from included studies: country, methodological approach, and use of conceptual framework; local/hospital HTA approach and activities described; reported effects and impacts of local/hospital-based HTA; factors facilitating/hampering the use of hospital-based HTA recommendations; and perceptions of stakeholders concerning local/hospital HTA. Due to the great heterogeneity among studies, we conducted a narrative synthesis of their results.ResultsA total of 18 studies met the inclusion criteria. We reported the results according to the four approaches for performing HTA proposed by the Hospital Based HTA Interest Sub-Group: ambassador model, mini-HTA, internal committee, and HTA unit. Results showed that each of these approaches for performing HTA corresponds to specific needs and structures and has its strengths and limitations. Overall, studies showed positive impacts related to local/hospital-based HTA on hospital decisions and budgets, as well as positive perceptions from managers and clinicians.ConclusionsLocal/hospital-based HTA could influence decision-making on several aspects. It is difficult to evaluate the real impacts of local HTA at the different levels of health care given the relatively small number of evaluations with quantitative data and the lack of clear comparators. Further research is necessary to explore the conditions under which local/hospital-based HTA results and recommendations can impact hospital policies, clinical decisions, and quality of care and optimize the use of scarce resources.

This study examines European decision makers' consideration and use of quantitative preference data. Methods:The study reviewed quantitative preference data usage in 31 European countries to support marketing authorization, reimbursement, or pricing decisions. Use was defined as: agency guidance on preference data use, sponsor submission of preference data, or decision-maker collection of preference data. The data could be collected from any stakeholder using any method that generated quantitative estimates of preferences. Data were collected through: (1) documentary evidence identified through a literature and regulatory websites review, and via key opinion leader outreach; and (2) a survey of staff working for agencies that support or make healthcare technology decisions.Results: Preference data utilization was identified in 22 countries and at a European level. The most prevalent use (19 countries) was citizen preferences, collected using time-trade off or standard gamble methods to inform health state utility estimation. Preference data was also used to: (1) value other impact on patients, (2) incorporate non-health factors into reimbursement decisions, and (3) estimate opportunity cost. Pilot projects were identified (6 countries and at a European level), with a focus on multi-criteria decision analysis methods and choice-based methods to elicit patient preferences. Conclusion:While quantitative preference data support reimbursement and pricing decisions in most European countries, there was no utilization evidence in European-level marketing authorization decisions. While there are commonalities, a diversity of usage was identified between jurisdictions. Pilots suggest the potential for greater use of preference data, and for alignment between decision makers.

This paper provides a critical review of the burgeoning literature on social capital. While our focus is primarily on social capital's place in economics, we do consider its broader social science context. In recent years, social capital literature has produced many insights, however, a number of conceptual and empirical problems remain. In this setting, we trace a panorama of ideas of the principal theorists of social capital for then focusing us on the numerous critics whose it is the subject. Finally we provide recommendations for a prudent use of the concept.

Modern infusion pumps widely used in hospitals in Quebec and elsewhere produce non-threatening levels of haemolysis during the transfusion of packed RBCs aged from 10 to 28 days. ASVs appear to induce additional haemolysis, and we do not recommend using them for blood transfusion.

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Objectives We aimed to elicit preferences of the French-speaking Quebec population regarding a COVID-19 vaccination program and to characterize individuals with respect to their vaccination behaviors. Methods A discrete choice experiment was conducted in Autumn 2020 via a web-based survey. Its design included seven attributes: vaccine origin, vaccine effectiveness, side effects, protection duration, priority population, waiting time to get vaccinated, and recommender of the vaccine. Utilities were estimated using a mixed-logit model and a latent class logit model. Results Our sample included 1599 individuals. From this total, 119 always chose the opt-out option (7.4%). According to the mixed-logit model, the relative weights of attributes were as follows: effectiveness (28.48%), side effects (23.68%), protection duration (17.41%), vaccine origin (12.75%), recommender (11.96%), waiting time to get vaccinated (3.62%), and priority population (2.11%). Five classes were derived from the latent class logit model. Class 1 (9.13%) wanted to get vaccinated as fast as possible and was composed of uncertain and more vulnerable individuals. Class 5 (25.14%) was similar to the full sample, mostly favoring vaccination. Classes 2 (7.69%) and 4 (15.82%) included “vaccine hesitant and demanding” individuals but were different in their sociodemographic profiles. Finally, “anti-vaccine” and other “vaccine hesitant” individuals were in class 3 (42.21%). Conclusions This study showed the vaccine characteristics that are likely to improve vaccine uptake, which may more easily lead to herd immunity. Different profiles of respondents also showed various levels of acceptance toward a COVID-19 vaccination program, which may help to better understand vaccine hesitancy behaviors. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01124-4.

Economic assessment is of utmost importance in the healthcare decision-making process. The quality-adjusted life-year (QALY) concept provides a rare opportunity to combine two crucial aspects of health, i.e., mortality and morbidity, into a single index to perform cost-utility comparison. Today, many tools are available to measure morbidity in terms of health-related quality of life (HRQoL) and a large literature describes how to use them. Knowing their characteristics and development process is a key point for elaborating, adapting, or selecting the most well-suited instrument for further needs. In this aim, we conducted a systematic review on instruments used for QALY calculation, and 46 studies were selected after searches in four databases: Medline EBSCO, Scopus, ScienceDirect, and PubMed. The search procedure was done to identify all relevant publications up to June 18, 2020. We mainly focused on the type of instrument developed (i.e., generic or specific), the number and the nature of dimensions and levels used, the elicitation method and the model selected to determine utility scores, and the instrument and algorithm validation methods. Results show that studies dealing with the development of specific instruments were mostly motivated by the inappropriateness of generic instruments in their field. For the dimensions’ and levels’ selection, item response theory, Rasch analysis, and literature review were mostly used. Dimensions and levels were validated by methods like the Loevinger H, the standardised response mean, or discussions with experts in the field. The time trade-off method was the most widely used elicitation method, followed by the visual analogue scale. Random effects regression models were frequently used in determining utility scores.