Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
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The objectives of this systematic review were 1) to identify studies that assess the psychometric performance of the English-language version of 35 generic multidimensional patient-reported outcome measures (PROMs) for children and young people in general populations and evaluate their quality and 2) to summarize the psychometric properties of each PROM. ⋯ Overall, consistent positive findings for at least five psychometric properties were found for Child Health and Illness Profile, Healthy Pathways, KIDSCREEN, and Multi-dimensional Student Life Satisfaction Scale. None of the PROMs had been evaluated for responsiveness to detect change in general populations. Further well-designed studies with transparent reporting of methods and results are required.
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In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods-system dynamics, discrete-event simulation, and agent-based modeling. ⋯ Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.
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Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. ⋯ This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.
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There has been some controversy on whether the costs of omalizumab outweigh its benefits for severe persistent allergic asthma. ⋯ Although the cost-effectiveness of omalizumab is more favorable under the PAS price, it represents good value for money only in severe subgroups and under optimistic assumptions regarding asthma mortality and improvement in health-related quality of life. For these reasons, omalizumab should be carefully targeted to ensure value for money.