Chapter 9

Health intention and need satisfaction in a dynamic world

Prior to any planning and evaluation of interventions, public health planners should carry out needs assessments to understand baseline conditions such as community resources and needs. Such needs often have a cyclic existence, repeatedly arising and repeatedly satisfied by resources to reach a dynamic balance. In this situation needs and need satisfaction can be invisible without seeing the dynamic equilibrium.  I describe in this chapter several models of such dynamics, starting with a simple model of metabolic need and need satisfaction, in which an agent uses up energy at a certain rate, and thus needs energy at a certain threshold rate of supply. This physiological balancing mechanism interacts with a reward-circuit balancing mechanism, to stimulate or inhibit eating behavior. 

An important case of one balancing process affecting another is sensitization and desensitization of feedback.  Desensitizing feedback, in the sense of delivering more reward per action, such as more pleasure for each unit of food eaten, is likely to lead to more action. Desensitizing, or “blunting” of one’s feedback response to food leads to more eating, not less, and can lead to obesity.  Over-exposure to food can desensitize feedback in this way, and so physiological balancing is one a mechanism linking environmental conditions and overeating. The same type of desensitization occurs, much more strongly, with certain additive substances. Some researchers call this effect “wanting” more and “liking” less (less “pleasure” response).  Intention is high, but the feedback response is low, leading to more perceived need and less perceived satisfaction of that need. Our interactive balancing models are perfectly suited to these mechanisms, as we have clear definitions of intention, need satisfaction, social-environmental and physiological conditions (context).  We clearly define wanting (desire), relative to the gap between intended and actual conditions. We define pleasure, reward experienced as liking, in terms of a clear neurophysiological conditional-action mechanism.  Most importantly, we have models of the mechanisms of feedback between these elements of condition, action, predicted reward and actual experienced reward. We apply these mechanisms to model over-eating, using mechanisms we learned earlier including reward-circuitry (chapter 7), sensitivity (chapter 8), and metabolism (chapter 9).

We expand this metabolic need model to consumption of shared resources, public health resources, to satisfy need. We then discuss how to model what happens when agents’ actions affect not only their own condition of need, but also affects the condition of other agents.  Example models show the emergence of collective need and collective action to satisfy need.  After specifying health needs and need satisfaction in terms of IF-THEN statements, we implement these in simulations using NetLogo. With these simulations we can show how perceived need and experience stress vary according to relative rates of consumption of and supply of local resources. This dynamic approach may be indispensable when making allocation decisions, e.g., if we were to intervene by allocating resources based on a static measure of unmet need, we might mistakenly cutoff needed resources to agencies that have been successfully meeting their needs. Shortly after a health resource cutback in Baltimore, a syphilis epidemic arose very suddenly, a popular example of a “tipping point” (Gladwell 2000). One interpretation of that suddenness of outbreak was that the resources had met need, establishing equilibrium in syphilis control, such that a relatively small cutback would “tip” the spread of the infection into epidemic levels.  However, if we apply our behavioral avalanche model (chapter 5), we would predict that many different “tipping points” at all scales could occur at any time. That model shows how a steady increment up or down in need satisfaction could at any time produce large or small landslides or explosions in infections.

I offer examples from my own prior research based largely on questions structured in the conditional-action format necessary to understanding dynamics of public health need satisfaction by networks of agencies.  That research, based on surveys of several hundred public health departments, revealed how directors’ beliefs about satisfying local collective need was conditional upon both the need itself and also upon whether or not other agencies were meeting the need. Of course, these sorts of beliefs open up the possibility of the same type of public health cooperative dilemma we examined earlier, this time at the level of networks of public health organizations (chapter 9). Studying networks of such organizations, we often move from complexity to complication. One problem with the word complex is that it evokes complication, but these are distinct concepts.   Complex systems arise when collective properties emerge from simple rules. Complicated systems, by contrast, do not necessarily have at root some simple underlying action. Specific public health or human service agencies have their own unique characteristics, stemming from unique histories in their local jurisdiction. A network of public health organizations, as in any given county, is likely to be quite different from others, despite our many attempts at categorizing types, say local health departments and so on.  Nonetheless, it can be useful to map out networks of organizations, especially as a prelude to developing needs and capacity assessments and program logic models.

Formally articulated processes of community health needs assessment include mapping “forces for change” (Gilmore and Campbell 2005) and, more generally, stimulating “systems thinking.” We can do this with dynamic modeling of health needs satisfaction. This requires showing the dynamics of satisfying physiological and social-behavioral needs with the needed resources, which in turn requires measurement of local needs, resources and their interaction over time. This interaction over time is the tricky part. Dynamic models sensitize us to the types of dynamic need satisfaction that occur; but, it is difficult to capture change with field techniques of observation and measurement alone (chapter 9).  We can create dynamic models of need including not only individual physiological health (a physiological balancing of metabolic costs and resources) but also, collective health as shared resources and costs. That approach extends naturally to the distribution of resources and costs across populations, as a measure of economic disparity and health disparity.