The Earth System Governance Project is the largest social science research network in the area of governance and global environmental change. I look forward to framing sustainability policy with systems thinking. I am affiliated with the Global Economic System Group. Check out the organization!
It is Official- My systems thinking/policy book has a release date of Nov 2014- Pre-order print or ebook @ Amazon and Springer website http://lnkd.in/bun2Dvs. @mbattlefisher- Just in time for holiday giving or course book selection!
There are times when we accept publically accept an approximation to move forward with life. Take, for instance, π (pi), better known for representing that irrational number of 3.14159 (and a lot of change) starring as the ratio of the circle’s circumference to its diameter. Yes, many of us recall our friend Archimedes and π from trigonometry class. Pi (π) is a mathematical constant derived from the area of a circle. Plug that π in to make sense of some spatial relationship. There is no riotous call for proofing unless it is on the exam or the SAT. Do students even proof anymore? The π is rationally accepted to serve a purpose to an end, to finish that darn homework on distributed systems. I will argue that the allegory of π can help us avoid clouded, myopic policy decisions.
A mental model is a cognitive tool. Success comes with altering those mental models. In other words, a mental model is not the dénouement. Formal simulations and mental models need each other. Mental models fall asleep on the job when complexity of the policy is dynamic (Sterman 2000). But it must be remember that policies are not spit out by modelling programs nor should they. So why am I calling upon modelling when policymakers already are bombarded with so much? I present the “policy π” in application of model testing to policymaking. “Policy π” focuses on making decisions that account for both systemic uncovered through the approximations garnered from formal modelling as well as practical issues posed in implementing the policy in the real world. Included in this “Policy π” is the common issue of policy ratification.
Regardless of the policy model used in a policy system,
when a new policy is written or committed in the system,
the administrator must consider [policy ratification as] how the new policy interacts with those
already existing in the system. (Agrawal et al 2005)
The model does NOT offer the “answer”. But the model is downright necessary to understand what is going on. When people brain storm, we battle with mental (qualitative) models pieced together as divergent points (inputs). We have all been there. Mentally, we approximate all the time. It is a natural part of the policy process. To the miasmic stench of permanent markers littering a flip chart, the decision makers and perhaps a silent minority are left to make sense of that complex data to get at the process that has no obvious end in sight. There needs to be “reflective conversation with the situation (policy )”of the simulated results (Schon 1992). The model does NOT forecast. The model may not be able to capture all of the connections to other policies that it is related to. It is a simulation. It is a model that is based on decision rules. It is a network with a priori boundaries.
Even with the best minds around the table, there still must be a mechanism to unleash the mental models around the table. Just as health policy would not move forward without epidemiology evidence, so should be the place of systems thinking in the policy process. Systems offers math to support or refute initial reactions to early conditions viewed under a policy. But the model will not be a predictive crystal ball. Much chatter has been circulated about the overreach of models into the world of prediction in policy. Agreed. But the meat of policy is not the model. In my opinion it should not be viewed in that way.
The policy π supported by the model, may give you sweet potato when the table (policymakers) ordered and expected the boysenberry that is now out of season. There may still be that flaky crust and reasonable price. But the input of a tuber over a berry is a notable change. Some of the ingredients may change. The taste will differ. Cooking time may change. You may not like boysenberry. The change to sweet potato could require a new produce distributor, who must then must recalibrate (with excitement) accommodating the needs of a new customer. Your fellow diners may try to convince you to hold out for boysenberry though there is a chance that maybe this sweet potato pie may remind you of Grandma’s. Sometimes a model changes that policy “pie” expectation. The model will not cover every possible scenario. Can you accept the presentation of the sweet potato? And perhaps next week, once again upending your policy gastronomy? Yes, a pie is a pie. Both a sweet potato and a boysenberry are dicotyledonous. Few would confuse the two. Each would present its own unique set of growth habits and susceptibilities. But food stuffs present different phenotypes and culinary experiences. Policy satiates, leaving some bellies full while others push away from the table wanting. Each policy π must be approached and respected as a necessity to prudent, systemic action to debate, not as a letdown of the expected π wished to grace the plate. How filling was that policy pi?
Agrawal, D et al (June 2005) Policy Ratification. Presented at Policies for Distributed Systems and Networks Sixth IEEE International Workshop
Schon D (1992).The theory of inquiry: Dewey’s legacy to education. Curriculum Inquiry. 22(2), 119-39
Sterman J (2002) All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, 18, 501-531
The Complexity Explorer project, being developed by the Sante Fe Institute, has included my Social Networks and Health course syllabus in its depository of course syllabi that offer instruction on complex systems science. Though I have since worked on some revisions, this is the second organization that has highlighted my course. The syllabus was first selected for the KaiserEDU course depository (since taken offline). I am working on integrating my new forthcoming book more prominently in the new syllabus.
Founded by Jon Wilkins, formerly associated with the Sante Fe Institute, the Ronin Institute is flipping and transforming the nature of academia. The Ronin Institute houses established researchers of various disciplines that reside mostly outside of academic institutions. I do not need to stress that there are a large number of could-be researchers who do not make it or do not care to be a part of the traditional university system. The popular code for what we do is ‘fractional’ research. Fractional or not, we are a group of strong researchers that kick butt and have bonded together to have a collective voice while adding to scientific knowledge.
Some press about the Ronin Institute:
2. http://www.bostonglobe.com/ideas/2012/05/26/new-idea-for-unemployed-academics/UUZOGe1KNWvUXDl7Yae1IL/story.html- We ain’t wayward! We are Ronins!
Steven Johnson (2010), the popular author of “Where Good Ideas Come From- The Natural History of Innovation”, wrote that innovation is structure based. A cornerstone of Johnson’s (2010) book is this idea of an informal, liquid network in which ideas collide. Johnson (2010) visualized the liquid network as the Hogarth’s painting “Humours of an Election”. Ideas generate from informal meetings not that company annual retreat in Florida ac-cording to Johnson (2010). The closer the people’s ideas are in space and time, the better the chance for the ideas to collide with a few new (idea) bonds formed along the way. Johnson’s use of network would better be labeled as idea management rather than being complexity driven. But for policy to be able to account for the long-term implications of idea management, ethical deliberation drives the complexity of organization. The included table compares the offerings of the liquid network of Johnson (2010) to the social networks for idea management. Collision unfortunately lacks the formality and accountability afforded by the structural relationships of social networks. Broadening idea generation beyond a “bump” illustrates the immensity of the effect of the social and political reach of policy.
People connect within a social space. According to Alba and Kadushin (1976), the measure of proximity is concerned with pairs of individuals and how “distant” they are. Traditionally, proximity is all what diffusion of social commodities, often defined by information sharing, as a flow (Alba and Kadushin 1976). In a liquid network of Johnson’s conceptualization, ideas bump into each other due to the act of discourse as an event. First, there must be connections to other people and the environment supports the development of ties. Johnson concurs. There are the things colliding in a network which in Johnson’s (2010) case is the idea. Discourse turns that “private solid state to a (public) liquid network” (Johnson 2010). In what Johnson (2010) called the state of adapting exaptation, people can informally engage due to the “bump factor” of these chance meetings (Johnson 2010). To Johnson (2010), innovations linger in a “slow hunch” countering the eureka moment. The power of leverage points values the ability to find a small change that can make a huge impact. This is not the same as the epiphany. Systems that undulate slowly can be most frustrating to the urgency placed on having policies work.
The liquid network is mildly bound enough to be a network but not enough to strangle innovation with rigidness. Ideas are innovative because of the people that offer them with their skills and prestige. Some ideas are drowned out or flatly ignored. Connecting the ideas as a structure is often descriptive. Which idea (or person with the idea) was most connected? Which idea structurally demonstrated power in the network of ideas? Then this whole idea of playing with private ideas within a public space by basing it on the structure of the irrational agents is the more interesting, and fruitful question to pose.
Johnson (2010) proposed that platforms, or orchestrated spaces, may foster innovation. Within these platforms, liquid networks which do not restrict movement within an organization and their innovation ideas may be better supported to thrive. The ideas take on a fluid state without outside restriction. Does the event become more consequential than the interaction supported by the event? Networks are systems in which the nodes (the things that are connected) are connected by ties. When one is interested in reasons for why people affiliate over an idea, two mode networks could be employed (Borgatti and Everett 1997). The people would be agents (set 1 mode) while the IDEAS serve as a “series of events” (set 2 mode) that they are sharing. In a two-mode network, both the idea and agent indicates the elements of the network. The idea becomes an element to be weighted with the agents and chance meetings. So there is a tie be-tween the agent and the event. The tie formation could be due to “bump factor”:
1) to accept the chance meeting and
2) engage in the innovating chance meeting.
Both nodes engage in it since Johnson (2010) is basing adapting exaptation on engagement not its possibility. But a concern with approaching such two-mode networks, which mode takes the primary position? For instance, Johnson (2010) does not divorce the person from the idea and but for his paradigm the idea is element of focus. The idea is the primary node. The idea comes from the person (secondary node). The argument would also be made that if innovation is viewed as a dynamic, social experiment, the person as the primary mode may say more about why that idea even came to be in the first place. While the platform may be nature be useful in revisiting existing resources such as ideas in a liquid network, the problem often uncovered in policy is the lack of new ideas and continued reliance on existing ideas that may lead to attribution errors. To Johnson (2010), the ideas are used as bonding agents. Two-mode networks deal with the nature of affiliation. The idea is no longer just assumed but is now used as a means to investigate the structure in which ideas are embedded within the ties connecting the author of the idea (see Smith and Christakis 2008). The approach of Johnson (2010) had ideas brought “together” by its messengers as one frame. I do not believe that Johnson (2010) is marginalizing the person behind the idea of the liquid network. By stressing the network, the structure at two mode levels appears to capture the reality of how innovation in a liquid network more likely occurs.
To Johnson (2010) removing boundaries to the liquid network leads to chaos. This may be wise as any chaotic system does not return back to original state before the spiraling began. This chaos could hamper innovation by changing the new starting point and disrupting the momentum started in creating the innovative (see Feigenbaum 1983). As Johnson (2010) was intending avoiding chaos for a malleable liquid state, there is no history of events connected with a state of chaos. When things are chaotic, the past matters far less, only the energies running amok are important. What significance might this hold for developing innovation? Unlike chaotic systems, complexity is tied to its history of events upon which the system was built (see Buchanan 2000).
What is applicable to policymakers under liquid networks, according to Johnson, is less promising. In offering the example of Howard Dean’s Presidential Campaign “supernova”, Johnson (2010) admitted that “political leadership involves some elements (decision making and oratory) that aren’t best outsourced to a liquid network.” So how do policymakers innovate and think creatively under political stressors? Using Wasserman and Faust (1994) taxonomy, two-mode networks have three possible purposes under Johnson’s (2010) liquid networks:
(1) The affiliation is the tie of the person/ideas (node) to the informal chance meetings (event).
(2) The “bump factor” (if it supports interaction) may make interaction, therefore innovation, more likely.
(3) In order to framing the adapting exaptation realistically, the measurement of homophily is required.
The two mode network is difficult to interpret, let alone analyze. But how ever said that policymaking was easy? People tend to clique around policy issues, perhaps based on the process of idea generation or even bunch out due to personal access due to co-location. Two modes capture this bunching up as cliques that overlap, perhaps by party affiliation or side of a legislative issue. But the point of this methods tale is not the complicated analysis but that that coming together to work policy out that on the surface can be deceivingly simplistic. This is a great example of how the mental model of a liquid network does not apply neatly to policymaking but still has a place in the discussion. Unlike the informal liquid network, the innovation required by policy must translate into proper strictures under governance. The chaos of a liquid network must be operationally linked to the structural nature of complexity. But this fact does not mean that networks, whether liquid or mathematically understood, are not useful in discerning policy. While the informal ideas that are eventually brought to the policy table may utilize adapting exaptation, the policymaking process strips away the lacquer of casualness.
Alba R, Kadushin C (1976) The intersection of social circles- a new measure of social prox-imity in networks. Socio Meth Res 5(1): 77-101
Borgatti S, Everett M (1997) Network analysis of 2-mode data. Soc Network 19: 243-269
Buchanan M (2000) Ubiquity- The Science of History…Or Why the World is Simplier Than We Think. Weidenfeld & Nichols, London
Feigenbaum M J (1983) Universal behavior in nonlinear systems. Physica 7: 16-39
Johnson S (2010) Where Good Ideas Come from. Riverhead-Penguin, New York
Smith K, Christakis N (2008). Social Networks and Health. Annual Review of Sociology 34: 405-429
Social policies are, more often than not, framed with the traditionalist rationalization of human intentionality. Be that as it may, policy tenders the protocols that are then acted up-on publically to bring social impact. Of course, a well-intended health policy must take into account on courses of action as well as funding priorities and constraints. I argue that socially based complexity puts into question the probability of purely rational public action. Social elements activated or retarded in a public policy can shift burden from one part of a healthcare system to another. In its most simple explanation, increased positive screening for disease within the safety net can lead to the probable increased usage of acute care treatment for individuals requiring more complicated care. While some level of desired social and health satisfaction may be experienced in the short term by shifting policy priorities, it is also probable that no tangible value is achieved toward to the overarching desire to elicit system wide impact. Will the positive changes last? I purport that living an illness with a public further complicates policy issues of keeping anything that is personally health related purely private.
According to network theory, naming a network is powerful. According to Trotter (1999), the existence of a boundary is defined by the rules of exit and entry. However, complex systems call for more intricate examinations of such boundaries. Unnamed groups are often identified by the observer and the boundaries are often most not agreed upon by the group members (Kadushin 2012). How might this idea work for special interests groups in making cohesion? According to Kadushin (2012), “a collectivity is structurally cohesive to the extent that the social relations of its members hold it together.” Further there are two mechanisms that support and disrupt this happy state of togetherness. First, if a “disruptive force” acts upon the group, will the network survive? Second, complexity is bound also to the health of its network. Like a game of Ker Plunk, disruption in a complex system occurs when one or more people are removed from the group (Kadushin 2012). The cohesion may or may not be able to survive. Then the process of community starts all over again with new set of actors and new structural relationships.
I do not recall an ICD code for attending family barbeques or activating one’s “social network” for staying healthy. Health care is not directly rewarded for healthy patients’ visits to Disneyland and the strength of the social support ties that keep patients well. More often than not, healthy patients demand less utilization of an already expensive and taxed health care system. Patients have social networks of confidantes of differing yields and compositions, but each member by association has the ability to persuade and dissuade if they wish. Often this network is an 800-pound gorilla in the examining room. This gorilla is a relative that has diabetes and complains of diabetic neuropathy while carefully sectioning the pecan pie with a surgeon’s precision. The sorority sister is a helpful “gorilla” that caresses your hand as you await medical results.
Failure is picking up a socially expected square peg after the innovative oval one fails to fit a conventional hole. If you really “need” the oval to work (and the world is not yet with the program), check out the board again. If there is no oval hole, darn it and chuck that board. Find a reamer and create your own or perhaps ask for a refund with no return shipping. Failure is the incessant attempt to satisfy others by hiding that socially acceptable square peg behind your back and asking for a few more days (in dog years) to work it out. Whittling that square peg with that dull pocket knife into a misshapen imposter of an oval peg serves no god. That imposter peg is not flush to the side of the hole. It is surrounded by slight flashes of open space. That open space created around the non-flush peg should extract with a slight tug. Trust me, that tug will be less taxing than the linear process that got that wrong peg there in the first place. Policy has little tolerance for misshapen pegs that bring with them unintended effects. Use a policy that works until it does not or admit that it never worked at all. Then make it work without the attribution errors gumming up the machinery. What works may not be the most apparent or popular choice.
At its simplest denominator, a citizen is by principle afforded the right of being included in a group’s decisions. But there is a special place for those who serve as policy experts. Sure, we could discuss until we are blue in the face how much a weight a vote in a representative democracy really holds. When I think of my job of being a citizen of any group, I am accountable in some manner to the group if I am not gerrymandered out of the process. Not unlike the idiom “we are in this together”, this cannot be truer in terms of health burden. The solidarity means that all of us have culpability in the collectives’ improving health. But each of our investment in this solidarity differs in our (re)actions, invocations and values. This knowledge should, in theory, affect the role that each of us plays in bettering health out-comes. But can and will citizenship overcome the medical reality that years of collective neglect have brought? How do we get people to give a darn and become a card-carrying Norma Rae? Those in policy hold a special role that should not be understated. A policy has the power to guide and mold the direction of societal movements or evade an unfortunate set-back. We are accountable but that job responsibility came with the rocky terrain. Necessary insights are gained from this systems approach. What is called for is the acknowledgement of the ligand and substrate nature of the two. In that regard, often a slanted pairwise comparison of objectivity to systems demonstrates the bias toward linearity. It is time for systems thinking to no longer be relegated to the kids’ table, peering around the corner and straining and wishing to bring its expertise to policy discussions.
Kadushin C (2012) Understanding Social Networks. Oxford, New York
Trotter R (1999) Friends, Relatives and Relevant Others: Conducting Ethnographic Network Studies. In: Schensul J, LeCompte, M, Trotter R, Cromley E, Singer M (eds.) Mapping social networks, spatial data and hidden populations. AltaMira Press. Lanham, MD
I am pleased to announce this article as an interview based on this research is highlighted in my book!!!
Using systems thinking in state health policymaking: an educational initiative
Karen J Minyard, Rachel Ferencik, Mary Ann Phillips and Chris Soderquist
Health Systems advance online publication 17 January 2014; pp. 1-7. doi: 10.1057/hs.2013.17
In response to limited examples of opportunities for state policymakers to learn about and productively discuss the difficult, adaptive challenges of our health system, the Georgia Health Policy Center developed an educational initiative that applies systems thinking to health policymaking. We created the Legislative Health Policy Certificate Program – an in-depth, multi-session series for lawmakers and their staff – concentrating on building systems thinking competencies and health content knowledge by applying a range of systems thinking tools: behavior over time graphs, stock and flow maps, and a system dynamics-based learning lab (a simulatable model of childhood obesity). Legislators were taught to approach policy issues from the big picture, consider changing dynamics, and explore higher-leverage interventions to address Georgia’s most intractable health challenges. Our aim was to determine how we could improve the policymaking process by providing a systems thinking-focused educational program for legislators. Over 3 years, the training program resulted in policymakers’ who are able to think more broadly about difficult health issues. The program has yielded valuable insights into the design and delivery of policymaker education that could be applied to various disciplines outside the legislative process.
Let us begin with the pressing policy issue of HIV risk taking behavior among homelessness teens to take home this point of social bond (de)construction. In the work of Rice et al.’s (2012) study of HIV risk behavior, homeless adolescents were located within the core (that dense ball of spaghetti in the middle of the network graph), were more likely to be female and were more likely to have been homeless for at least 2 years. The longer the teen, particularly for the young woman, is outside of the family unit, the teens form strong, compact ties with a new “family”. Surprisingly, being on the outside of this tight “family” that is found in the periphery of the network was protective against HIV risk taking. Highly connected, dense core are great for galvanizing information within that group. But a dense group may be more difficult to infiltrate. If the dense ball of teens are passing misinformation and reinforcing risky HIV behaviors, it is best to go your own way. But where can a young person go with such marginalized circumstances?
If the public policy being developed pertains directly to HIV risk taking reduction, perhaps targeting the core network to diminish risk could be a first step. But in the work of being connected to other people, the low risk teens may help each other or could transfer into the high risk group. But policymakers must remain mindful of what systemic changes can flow from targeting that portion of the network. People come and go into each other’s lives. Policy must be mindful that the longer the teen is outside of a traditional household, human connections will be made with the people that they have the most contact with. Could the teens in periphery have formed cliques that supported less risk taking? This may help these teens. Keep the periphery teens supported in their low-risk behavior. In the world of networks, there is something called homophily (birds of a feather). This means more than living in the same place. Above that shared space, the teens are ties together by something stronger: love, support, shared values, shared behaviors (see Feld & Carter, 1998; Kadushin, 2012). In other world, people live by forming bonds wherever they land.
If the policy lumps the new cliques (core and periphery) together, network membership can change over time. Teens that tie together two completely separate networks are called bridges. By theory, the networks would not have connected if not for this new bridge. Often the bridge has enough prestige and power to convince two divergent groups to join forces (Granovetter, 1973). Will the new members from the outside possess adequate social currency to offset the peer influence of the core members? Thus we have complexity. Can one policy that is meant to affect teens as if they share the same life chances and social embeddedness work? Most likely answer is no. While there may be an overarching goal set up the policy, parse out how different attributes of the teens may affect how the proposed policy works.
The longer a teen is away, it becomes more likely that their family will be in the same dire social straits and may not be protective in navigating good social choices and decisions. But the longer a teen is away, human nature requires connection and closeness, a family broadly defined. Being on the outside (periphery) of the homeless core protects against HIV risk-taking. Let us not forget a social purgatory between the instability of homelessness and the perceived caustic environment that the teen desperately calls to escape. The peripherals may be at risk in other ways that may lead to a greater risk of HIV risk taking once the teen is in the core. But if that teen has the ability to persuade those at risk, there is a possibility that the low risk taking of a strong teen could start to cascade low risk attitudes and values. But there is also a possibility that the teen will become enveloped and become high risk himself. It may be too much to ask of that teen to work to overhaul the collectively held value of higher risk sexual practices (Long et. al., 2013).
So in using the research on social networks, I propose systemic factors that should be accounted when attacking HIV among homeless teens:
1. Every homeless teen is not the same and each with present a different set of connections.
2. Being deeply connected in the homeless culture may place these teens at higher risk for unsafe sexual behaviors.
3. Targeting low risk teens on the periphery will require a different intervention to support the low risk behavior.
4. While there may be opportunities to use low-risk teens as “bridges” to the high-risk teens, this should only be done with extreme care and oversight. The bridge is more susceptible to falling into the activities of the core and may suffer from burn-out for the heightened sense that change is on that teen’s shoulders.
5. Watch the movement of teens from the core to periphery (and back again). This movement brings a whole new set of structural realities both for the teen as well as the network.
Social networks are powerful and are often underutilized in uncovering the underlying structure of health policies. But the policy work that we should hold dear must account for the power to combatting ecological gaps and failures, such as the personal and societal failing of just one homeless teen.
Feld, S. & Carter, W. (1998). “Foci of Activities as Changing Contexts for Friendship.” In
Placing Friendship in Context, eds. Rebecca G. Adams and Graham Allan. Cambridge,
UK: Cambridge University Press.
Granovetter, M. (1973). The strength of weak ties. American Journal of Psychology. 78 (9),
Kadushin, C. (2012). Understanding Social Networks. New York: Oxford.
Long, J., Cunningham, F. & Braithwaite, J. (2013). Bridges, brokers and boundary spanners in collaborative networks: a systematic review BMC Health Services Research 2013, 13:158
Rice, E., Barman-Adhikari, A., Milburn, N. & Monro, W. (2012) Position-Specific HIV Risk in a Large Network of Homeless Youths. American Journal Of Public Health. 102(1), 141-147.
NOTE: This white paper is a revision of a blog written by the author. An early version was originally posted on the Orgcomplexity Blog (Orgcomplexity.wordpress.com) on February 28, 2013.
It is hard to be strong in fortitude based solely on principle when you making that action alone. We co-exist with others. Policy is there to help orchestrate a health strategy to support these socially accepted goals of better health outcomes. The point of better health is to reduce pain and cheat death as long as possible. In this quest, is a clinically imperfect body one owned or owed to society? We in health policy need to take a moment to ponder this question. Whose body is it anyway that the policy is built around? What can be held as private when the collective’s health is at stake? People are embodied. Simply, each person has an acknowledged connection to his or her body. The sum of those bodies comprises the target populations that public health sets out to help. Our public health initiatives affect the whole “body” of society. Torjman (2005) put it succinctly, “At the end of the day, the formulation of public policy involves a process of making good decisions – for the public good”. Public health by nature deviates from clinical medicine in the sense that now public health is the overseer in your home, with the hopes of paternally people to leading more healthful lifestyles and improved quality of life. The narrative and public outcries can give perspective and requirement to act but above all we must make sure the policy priorities cannot be divorced from accepted evidence (Niessen et al. 2000). We in health policy are in the business of working magic to usher legislative and political changes while leapfrogging with human agency. Policy is called to strike the perfect balance between finessing risk in its favor with the most economically reasonable actions with the least of amount of societal discord. Under these terms, policy is considered effective (Nagel 1986).
Hovland (2007) warns of the gap of influence that peer reviewed research may have on policy that must be responsively more rapidly. As Hovland (2007) said, it is all about impact. I add that it is all about systemic impact over time. There are policy outfits that are more the research institute and may have more comfort in this call for evidence. Sentiment drives policy. However, data framing must ground our policy in response to social sentiment and political pressure. Calculated risk may fail to not account for are the specific systemic factors such as special interests, social media blitz or dwindling National Institutes of Health (NIH) public health funding are blockages that policy people know all too well.
The dinged battle armor of policymakers continues to be tested at every turn in this regard. Policy acknowledges the system working with supportive and against opposing social issues. Sometimes the opportunity is not taken or afforded to reintroduce the public to what policy does. In the arduous work in policy making, public health burdens change as it should in tandem with changing policy. Public health data is our friend and should remain so but sometimes policy needs an epidemiologist on speed dial. Then it is off to the races, the policy races, that is. There is no Derby Horseshoe Wreath and throngs of adorning fans at the finish. There is only a sneaky reposition of the finish line and some incremental improvements and setbacks in the state of public health along the way.
Public health calls for the judicious selection of targeted priorities. The U.S. Department of Health and Human Services’ 10-year “Healthy People” assessments tell us so and we heed the foreboding .There are times when the public require action on a health issue, that while worthwhile, that may not jive with the personal or even collective wants or the scientific evidenced priorities. Policies may react to an outcry of an event or issue that merits immediate action to a risk factor (reactive policy) (see Torjman 2005). Policies may be made for the short term or the long term concerns. We often hear of an Act being passed “in response to” an acute problem in hopes of reversing and/or reducing any residual effects of an acute event. The exigency of a particular situation demanding does not excise the fact that care must be taken in its development and implementation. However, network research by Crane (1991) has shown that if a problem from sparse to more populated problems may more quickly spread than anticipated. Health policy has to worry about medical as well as systemic spread.
“… if the incidence [of the problem] reaches a critical
point, the process of spread [within a network] will explode.” (Crane 1991)
The magnitude of the coverage of the health policy in part is defined with epidemiological evidence over time or proof of an emergent need happening now. Often in the aftermath of activating the existing policy, new developments that make evident the need for tweak or overhaul appear during the act of its use. Assessment while doing (such as outcome evaluation) is often necessary, good business. System based assessment for future policy allows for simulation of ready data to anticipate “what ifs” versus real-time trials where it is baptism by fire with no safety harness.
If a policy is afforded the liberty to be worked out over time without duress, policy stakeholders may relish in the ability to break down and reassess the policies before implementing them into action. But public health always has fires to put out. The epidemiology continues to shift and react to health actions and reactions, outdating the numerator. Often that urgency, which warranted, can cloud perception and impair the ability to notice critical issues undergirding the health emergency. Which public health issue cuts to the front of the line, the funded research priority or one seizing social outcry?
Health is grounded in the reducible epidemiological data and irreducible private experiences of the patients. The requirement to ground proactive policy in peer reviewed or trusted evidence is not without merit. Evidence is non-negotiable. I insist that enlisting the available evidence and observations into models. A model uses cadres of data to represent more simply what could occur in a system. There is a system beyond the static rates.
Crane J (1991) The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing. Am J Sociol 96: 1226-1259
Hovland, I. (2007). Making a Difference: M&E of Policy Research. Overseas Development Institute. http://www.odi.org.uk/sites/odi.org.uk/files/odi-assets/publications-opinion- files/2426.pdf. Accessed 10 April 2014
Nagel S (1986) Efficiency, effectiveness and equity in public policy evaluation. Rev Pol Res 6(1): 99-120
Niessen LW, Grijseels EW, Rutten FF. (2000) The evidence-based approach in health policy and health care delivery. Soc Sci Med. 51(6):859-869
Rose G (1985) Sick individuals and sick populations. Int J Epidemiol 14: 32–38
Torjman, S. (2005). What is policy? http://www.caledoninst.org/publications/pdf/544eng.pdf.
Accessed on 5 December 2013