For example, directed acyclic graphs (DAGs) are frequently used in CIM, but DAGs are unsuitable for modelling systems containing feedback loops (given they are acyclic), a common feature of complex social systems. However, CIM as applied in epidemiology today, has coalesced around a set of tools with certain limitations when applied to complex systems. The rise of causal inference methods (CIM), has attempted to address this by providing a framework under which we may predict the outcomes of proposed interventions. Health policy-making is, at the root, an attempt to undertake principled decisions in an environment of high uncertainty and high risk, in which competing pressures and interests from stakeholders have a significant effect on the actions ultimately taken. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods.
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