Welcome to the Website of the 3rd Annual Workshop on

Dynamic Modelling for Health Policy:  Understanding Social Determinants of Health
& Reducing Health Inequities

Workshop orientation information and the workshop agenda are now available for download by participants

July 18 - 20, 2011 University of Saskatchewan

Saskatoon, SK, Canada

The Annual Workshops on Dynamic Modeling for Health Policy is a forum for convening small groups of mathematical/computational modelers, policymakers, community organizations, and domain experts who share a common interest in applying dynamic modeling to pressing health policy concerns. The theme of the third workshop in this series is leveraging such modeling to better understand the broader determinants of health, and to produce momentum for promoting equitable health for all.  Particular emphasis will be placed on the potential for the modeling process to serve as a catalyst for change within communities, organizations, and governments.

Why Determinants of Health?

While traditional reductionist methods have offered great benefits to human health, health disparities remain stubbornly persistent, and are growing in many areas. These disparities, many rooted in social injustice, cause modifiable and preventable human suffering, and burden the health and wellbeing of all. In spite of growing understanding of the importance of social conditions in shaping population health outcomes, comparatively little is known about their interconnections, and how they operate across different population subgroups over time.

Why Dynamic Modelling?

The workshop will focus on the potential for systems models and the modeling process to bring insight and catalyze change with respect to social disparities. Despite widespread recognition of the significance of identifying and promoting policies that address health disparities, the origin and policy implications of such disparities are dauntingly complex to unravel. While traditional analytical tools can offer much insight into the extent of these disparities, there are significant methodological challenges in understanding the etiology of such disparities. These challenges reflect, among other things, the wide variety of interacting factors playing out over the life of an individual, the broad ranges of timescales involved, the multiple levels of causation and effect (e.g. physiological and psychological mechanisms within individuals, surrounding layers of social and institutional context), widespread heterogeneities, the presence of bidirectional, delayed, gene-environment interactions, and discontinuous and time-varying causal interactions between factors within and between scales. Because of the interaction of diverse factors in shaping outcomes, there are also methodological challenges in using traditional approaches to identify the effective, robust and cost-effective ways to decrease such disparities. Studying such complex linkages purely using one methodology or tool is challenging and, all too often, ineffective. In recent years, researchers elsewhere in public health have overcome similar challenges by complementing traditional techniques with dynamic modeling. Such studies have shed much light on the interconnections between diverse causal factors, helped inform policy tradeoffs, interpret epidemiological trends, and prioritize data collection. Within the population health and health disparities area, a small but growing number of models have offered insight into aspects of health disparities. The importance and complexity of policy issues surrounding health disparities make it an attractive prospect for application of dynamic modeling.

Both qualitative and quantitative forms of systems modeling are also attractive due to their demonstrated potential for serving as agents of change within communities and organizations. Whether in the form of group model building at the inception of a modeling project, or through simulation learning environments encapsulating existing simulation models, the modeling process can engage individuals from broad walks of life and who lack any previous exposure to modeling. By providing a means of translating the implications of research understanding into outcomes familiar and important to community stakeholders, and by serving as a focus for dialogue concerning possible futures, the modeling process can aid disparate groups of stakeholders in overcoming barriers to collaboration, finding common ground, and coordinating more effectively.