Providing specific definitions for compliance and persistence is important for sound quantitative expressions of patients' drug dosing histories and their explanatory power for clinical and economic events. Adoption of these definitions by health outcomes researchers will provide a consistent framework and lexicon for research.

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. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. 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.

AIA is driven by CD4+ T lymphocytes. IL-6 is an important mediator of bone destruction in AIA because it regulates T lymphocyte production of key osteoclastogenic cytokines and inflammation-induced bone marrow osteoclast differentiation. These findings have implications for reducing bone and joint damage in rheumatoid arthritis.

RA is an autoimmune disease characterized by sustained imbalance between pro-and antiinflammatory immune mechanisms. The SOCS proteins are negative regulators of cytokine signaling, but to date there has been little information on their function in disease. The generation of Socs3 -/Dvav mice, which lack SOCS-3 in the hematopoietic and endothelial cell compartment, allowed us to explore the role of endogenous SOCS-3 during acute inflammatory arthritis. Joint inflammation in Socs3 -/Dvav mice was particularly severe and was characterized by increased numbers of neutrophils in the inflamed synovium, bone marrow, peripheral blood, and spleen. These features were most likely due to increased production of and enhanced responsiveness to G-CSF and IL-6 during arthritis in these mice. Local osteoclast generation and bone destruction were also dramatically increased in the absence of SOCS-3, as was macrophage activation. Finally, SOCS-3 was found to negatively regulate CD4 + T lymphocyte activation, including production of the pleiotropic cytokine IL-17. The absence of SOCS-3 therefore had dramatic effects in this disease model, with a broader impact on cellular responses than SOCS-1 deficiency. These findings provide direct in vivo evidence that endogenous SOCS-3 is a critical negative regulator of multiple cell types orchestrating inflammatory joint disease.