Optical diagnosis with NBI was found to be equally effective compared with the standard of care (WLE), while potentially enabling cost savings from the NHS England perspective.

Introduction Coronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints. Methods We modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use. Results We estimated that public sector contributions led to at least 30% reductions in COVID-19-related healthcare resource utilization. Private sector contributions to expanded diagnostic testing and treatments led to further reductions in mortality (− 44%), intensive care unit (ICU) and non-ICU hospital beds (− 30% and − 28%, respectively), and ventilator use (− 29%). The combination of lower diagnostic test sensitivity and proportions of patients self-isolating may exacerbate case numbers, and policies that encourage self-isolating should be considered. Conclusion While mechanisms exist to facilitate research, development, and patient access to diagnostic testing, future policies should focus on ensuring equitable patient access to both diagnostic testing and treatments that, in turn, will alleviate COVID-19-related resource constraints. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-020-01597-3.

IntroductionCoronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints.MethodsWe modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use.ResultsWe estimated that public sector contributions lead to ≥30% reductions in COVID-19-related healthcare resource utilization. Private sector contributions to expanded diagnostic testing and treatments lead to further reductions in mortality (−44%), intensive care unit (ICU) and non-ICU hospital beds (−30% and −28%, respectively), and ventilator use (−29%). The combination of lower diagnostic test sensitivity and proportions of patients self-isolating may exacerbate case numbers, and policies that encourage self-isolating should be considered.ConclusionWhile mechanisms exist to facilitate research, development, and patient access to diagnostic testing, future policies should focus on ensuring equitable patient access to both diagnostic testing and treatments which, in turn, will alleviate COVID-19-related resource constraints.

insights on model constructs, key data elements/assumptions, and recent modeling advances. Results: Thirty-three HTAs comprising 60 CUAs were considered relevant and investigated further. Albeit individual sampling models and discrete event simulations have some advantages over Markov models, these three techniques may provide similar cost-effectiveness estimates and were all deemed appropriate for HTA submissions. At least ten different structural components were identified for which data sources and/or assumptions have evolved over time, several of which have a major bearing on model outcomes. The characteristics of patients entering the model (e.g. disease severity and prior treatments), assumptions about long-term disease progression whilst on treatment and the rebound effect upon treatment discontinuation, and mapping of Health Assessment Questionnaire and/or pain scores to Quality of Life utility values were repeatedly mentioned as key elements affecting the results. ConClusions: A wide variety of economic models for the evaluation of bDMARDS in RA have been developed and are continuously being refined. Despite recent initiatives to reach consensus on how RA models should be designed, substantial differences in the data sources and assumptions that are used still remain. This limits the comparability across and also generalizability of the various results obtained by using these models and poses problems to all stakeholders involved in HTAs.

and Spain are compiled from IMS Health data from 2001 to 2013 and linked to a variety of country-level indicators that, based on prior literature, could explain observed differences in (1) overall oncology spending as a percentage of the country's total prescription medication budget, (2) share of generic medication utilization when a generic is available, and (3) the utilization share of different types of oncology therapies such as hormonal, cytotoxic, and targeted biologic treatments. Country-level factors used to explain this variation include cancer prevalence, per capita income, health insurance system classification, and access to physicians. A variety of econometric panel data methods are used to evaluate the associations between selected country-level indicators and the outcomes listed above. Results: Preliminary results suggest a moderate association between health system classification, physician access, and oncology medication spend. Specifically, we find that countries with social insurance systems with greater access to physicians are more likely to use targeted biologic medications in place of cytotoxics. The tendency to use biologics also explained most of the variation observed in generic oncology medication utilization. ConClusions: Differences in country-level oncology medication spending are primarily driven by a higher demand for targeted biologic medications. This demand can be partially explained by increased physician access and health insurance system type.