Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
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To identify generic, multidimensional patient-reported outcome measures (PROMs) for children up to 18 years old and describe their characteristics and content assessed using the International Classification of Functioning, Disability and Health Children and Youth version (ICF-CY). ⋯ A broad variety of PROMs is available to assess children's health. Nevertheless, only a few PROMs can be used across all age ranges to 18 years. When mapping their content on the ICF-CY, it seems that most PROMs exclude at least one major domain, and all conflate aspects of functioning and well-being in the scales.
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Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. ⋯ Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.
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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.