Conceptualising population health: from mechanistic thinking to complexity science

The mechanistic interpretation of reality can be traced to the influential work by René Descartes and Sir Isaac Newton. Their theories were able to accurately predict most physical phenomena relating to motion, optics and gravity. This paradigm had at least three principles and approaches: reductionism, linearity and hierarchy. These ideas appear to have influenced social scientists and the discourse on population health. In contrast, Complexity Science takes a more holistic view of systems. It views natural systems as being 'open', with fuzzy borders, constantly adapting to cope with pressures from the environment. These are called Complex Adaptive Systems (CAS). The sub-systems within it lack stable hierarchies, and the roles of agency keep changing. The interactions with the environment and among sub-systems are non-linear interactions and lead to self-organisation and emergent properties. Theoretical frameworks such as epi+demos+cracy and the ecosocial approach to health have implicitly used some of these concepts of interacting dynamic sub-systems. Using Complexity Science we can view population health outcomes as an emergent property of CAS, which has numerous dynamic non-linear interactions among its interconnected sub-systems or agents. In order to appreciate these sub-systems and determinants, one should acquire a basic knowledge of diverse disciplines and interact with experts from different disciplines. Strategies to improve health should be multi-pronged, and take into account the diversity of actors, determinants and contexts. The dynamic nature of the system requires that the interventions are constantly monitored to provide early feedback to a flexible system that takes quick corrections

Incentive payments are not related to expected health gain in the pay for performance scheme for UK primary care: cross-sectional analysis

Background: The General Medical Services primary care contract for the United Kingdom financially rewards performance in 19 clinical areas, through the Quality and Outcomes Framework. Little is known about how best to determine the size of financial incentives in pay for performance schemes. Our aim was to test the hypothesis that performance indicators with larger population health benefits receive larger financial incentives. Methods: We performed cross sectional analyses to quantify associations between the size of financial incentives and expected health gain in the 2004 and 2006 versions of the Quality and Outcomes Framework. We used non-parametric two-sided Spearman rank correlation tests. Health gain was measured in expected lives saved in one year and in quality adjusted life years. For each quality indicator in an average sized general practice we tested for associations first, between the marginal increase in payment and the health gain resulting from a one percent point improvement in performance and second, between total payment and the health gain at the performance threshold for maximum payment. Results: Evidence for lives saved or quality adjusted life years gained was found for 28 indicators accounting for 41% of the total incentive payments. No statistically significant associations were found between the expected health gain and incentive gained from a marginal 1% increase in performance in either the 2004 or 2006 version of the Quality and Outcomes Framework. In addition no associations were found between the size of financial payment for achievement of an indicator and the expected health gain at the performance threshold for maximum payment measured in lives saved or quality adjusted life years. Conclusions: In this subgroup of indicators the financial incentives were not aligned to maximise health gain. This disconnection between incentive and expected health gain risks supporting clinical activities that are only marginally effective, at the expense of more effective activities receiving lower incentives. When designing pay for performance programmes decisions about the size of the financial incentive attached to an indicator should be informed by information on the health gain to be expected from that indicator

The value of health care – a matter of discussion in Germany

BACKGROUND: Interest in assessing the value of health-care services in Germany has considerably increased since the foundation of the Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen, IQWiG (Institute for Quality and Efficiency in Health Care). The practical application of value assessment illustrates how problematic the process can be. In all decisions made for the provision of health care, data concerning the measurable dimensions (quantity and quality of efficacy and effectiveness, validity of the results and costs) flow into a complex and not yet standardized decision-making process concerning public financing. Some of these decisions are based on data of uncertain validity, unknown reproducibility and unclear appropriateness. DISCUSSION: In this paper we describe the theoretical aspects of value from psychological and economic viewpoints and discuss national and international approaches. Methodic details and difficulties in assessing the value of health-care services are analysed. A definition of the intangible value of health-care services will be proposed which contains only three factors: the absolute risk reduction (usually a measure of efficacy), the validity of the scientific papers examined and the type of the expected effectiveness (prevention of death and disability, restitution of well-being). The intangible value describes the additional benefit when comparing two possible actions, like treatment or observation only. CONCLUSION: The description of intangible value from the viewpoint of different stakeholders is a useful measure for subsequent steps (not discussed here) – the evaluation of costs and of patient benefit. A standardised, transparent, fair and democratic evaluation is essential for the definition of a basic benefit package

Analysing key influences over actors' use of evidence in developing policies and strategies in Nigeria: a retrospective study of the Integrated Maternal Newborn and Child Health strategy

Background Evidence-informed policymaking has been promoted as a means of ensuring better outcomes. However, what counts as evidence in policymaking lies within a spectrum of expert knowledge and scientifically generated information. Since not all forms of evidence share an equal validity or weighting for policymakers, it is important to understand the key factors that influence their preferences for different types of evidence in policy and strategy development. Method A retrospective study was carried out at the national level in Nigeria using a case-study approach to examine the Nigerian Integrated Maternal Newborn and Child Health (IMNCH) strategy. Two frameworks were used for conceptualization and data analysis, namely (1) to analyse the role of evidence in policymaking and (2) the policy triangle. They were used to explore the key contextual and participatory influences on choice of evidence in developing the IMNCH strategy. Data was collected through review of relevant national documents and in-depth interviews of purposively selected key policy and strategic decision makers. Thematic analysis was applied to generate information from collected data. Results The breadth of evidence used was wide, ranging from expert opinions to systematic reviews. The choice of different types of evidence was found to overlap across actor categories. Key influences over actors’ choice of evidence were: (1) perceived robustness of evidence – comprehensive, representative, recent, scientifically sound; (2) roles in evidence process, i.e. their degree and level of participation in evidence generation and dissemination, with regards to their role in the policy process; and (3) contextual factors such as global agenda and influence, timeline for strategy development, availability of resources for evidence generation, and lessons learnt from previous unsuccessful policies/plans. Conclusion Actors’ preferences for different types of evidence for policy are influenced not only by the characteristics of evidence itself, but on actors’ roles in the evidence process, their power to influence the policy, and the context in which evidence is used

Agent-Based Model of Therapeutic Adipose-Derived Stromal Cell Trafficking during Ischemia Predicts Ability To Roll on P-Selectin

Intravenous delivery of human adipose-derived stromal cells (hASCs) is a promising option for the treatment of ischemia. After delivery, hASCs that reside and persist in the injured extravascular space have been shown to aid recovery of tissue perfusion and function, although low rates of incorporation currently limit the safety and efficacy of these therapies. We submit that a better understanding of the trafficking of therapeutic hASCs through the microcirculation is needed to address this and that selective control over their homing (organ- and injury-specific) may be possible by targeting bottlenecks in the homing process. This process, however, is incredibly complex, which merited the use of computational techniques to speed the rate of discovery. We developed a multicell agent-based model (ABM) of hASC trafficking during acute skeletal muscle ischemia, based on over 150 literature-based rules instituted in Netlogo and MatLab software programs. In silico, trafficking phenomena within cell populations emerged as a result of the dynamic interactions between adhesion molecule expression, chemokine secretion, integrin affinity states, hemodynamics and microvascular network architectures. As verification, the model reasonably reproduced key aspects of ischemia and trafficking behavior including increases in wall shear stress, upregulation of key cellular adhesion molecules expressed on injured endothelium, increased secretion of inflammatory chemokines and cytokines, quantified levels of monocyte extravasation in selectin knockouts, and circulating monocyte rolling distances. Successful ABM verification prompted us to conduct a series of systematic knockouts in silico aimed at identifying the most critical parameters mediating hASC trafficking. Simulations predicted the necessity of an unknown selectin-binding molecule to achieve hASC extravasation, in addition to any rolling behavior mediated by hASC surface expression of CD15s, CD34, CD62e, CD62p, or CD65. In vitro experiments confirmed this prediction; a subpopulation of hASCs slowly rolled on immobilized P-selectin at speeds as low as 2 µm/s. Thus, our work led to a fundamentally new understanding of hASC biology, which may have important therapeutic implications

Staffing and Training Aspects of Hospital Management: Some Issues for Research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68624/2/10.1177_107755878904600205.pd

On the relationship between individual and population health

The relationship between individual and population health is partially built on the broad dichotomization of medicine into clinical medicine and public health. Potential drawbacks of current views include seeing both individual and population health as absolute and independent concepts. I will argue that the relationship between individual and population health is largely relative and dynamic. Their interrelated dynamism derives from a causally defined life course perspective on health determination starting from an individual’s conception through growth, development and participation in the collective till death, all seen within the context of an adaptive society. Indeed, it will become clear that neither individual nor population health is identifiable or even definable without informative contextualization within the other. For instance, a person’s health cannot be seen in isolation but must be placed in the rich contextual web such as the socioeconomic circumstances and other health determinants of where they were conceived, born, bred, and how they shaped and were shaped by their environment and communities, especially given the prevailing population health exposures over their lifetime. We cannot discuss the “what” and “how much” of individual and population health until we know the cumulative trajectories of both, using appropriate causal language

A study protocol for applying the co-creating knowledge translation framework to a population health study

BACKGROUND: Population health research can generate significant outcomes for communities, while Knowledge Translation (KT) aims to expressly maximize the outcomes of knowledge producing activity. Yet the two approaches are seldom explicitly combined as part of the research process. A population health study in Port Lincoln, South Australia offered the opportunity to develop and apply the co-KT Framework to the entire research process. This is a new framework to facilitate knowledge formation collaboratively between researchers and communities throughout a research to intervention implementation process. DESIGN: This study employs a five step framework (the co-KT Framework) that is formulated from engaged scholarship and action research principles. By following the steps a knowledge base will be cumulatively co-created with the study population that is useful to the research aims. Step 1 is the initiating of contact between the researcher and the study contexts, and the framing of the research issue, achieved through a systematic data collection tool. Step 2 refines the research issue and the knowledge base by building into it context specific details and conducting knowledge exchange events. Step 3 involves interpreting and analysing the knowledge base, and integrating evidence to inform intervention development. In Step 4 the intervention will be piloted and evaluated. Step 5 is the completion of the research process where outcomes for improvement will be instituted as regular practice with the facilitation of the community. In summary, the model uses an iterative knowledge construction mechanism that is complemented by external evidence to design interventions to address health priorities within the community. DISCUSSION: This is a systematic approach that operationalises the translational cycle using a framework for KT practice. It begins with the local context as its foundation for knowledge creation and ends with the development of contextually applicable interventions. It will be of interest to those involved in KT research, participatory action research, population health research and health care systems studies. The co-KT Framework is a method for embedding the principles of KT into all stages of a community-based research process, in which research questions are framed by emergent data from each previous stage.Kathryn Powell, Alison Kitson, Elizabeth Hoon, Jonathan Newbury, Anne Wilson and Justin Beilb

Exploring health systems research and its influence on policy processes in low income countries

<p>Abstract</p> <p>Background</p> <p>The interface between research and policymaking in low-income countries is highly complex. The ability of health systems research to influence policy processes in such settings face numerous challenges. Successful analysis of the research-policy interface in these settings requires understanding of contextual factors as well as key influences on the interface. <it>Future Health Systems (FHS): Innovations for Equity </it>is a consortium conducting research in six countries in Asia and Africa. One of the three cross-country research themes of the consortium is analysis of the relationship between research (evidence) and policy making, especially their impact on the poor; insights gained in the initial conceptual phase of FHS activities can inform the global knowledge pool on this subject.</p> <p>Discussion</p> <p>This paper provides a review of the research-policy interface in low-income countries and proposes a conceptual framework, followed by directions for empirical approaches. First, four developmental perspectives are considered: social institutional factors; virtual versus grassroots realities; science-society relationships; and construction of social arrangements. Building on these developmental perspectives three research-policy interface entry points are identified: 1. Recognizing policy as complex processes; 2. Engaging key stakeholders: decision-makers, providers, scientists, and communities; and 3. Enhancing accountability. A conceptual framework with three entry points to the research-policy interface – policy processes; stakeholder interests, values, and power; and accountability – within a context provided by four developmental perspectives is proposed. Potential empirical approaches to the research-policy interface are then reviewed. Finally, the value of such innovative empirical analysis is considered.</p> <p>Conclusion</p> <p>The purpose of this paper is to provide the background, conceptual framework, and key research directions for empirical activities focused on the research-policy interface in low income settings. The interface can be strengthened through such analysis leading to potential improvements in population health in low-income settings. Health system development cognizant of the myriad factors at the research-policy interface can form the basis for innovative future health systems.</p