Population Health Resources and Policy

Population Health Financing: Beyond Grants

My professional coming of age took place the late 1960s, in one of the original Office of Economic Opportunity (OEO) neighborhood health centers in the South Bronx. Because the health centers were a part of the larger federal antipoverty strategy, they were founded on a broad view of health (we would call it a population health framework even though that terminology didn’t exist then). Health care innovation was the core, with community health workers, health care teams, and the understanding that the residents who didn’t use the clinic contributed to overall neighborhood heath as much as those who did.

But the OEO funding paid for much more than health care, including job training, legal advocacy, school health programs, neighborhood built environments – what we now see as the multiple determinants of health. But one of the main lessons of my entire career was the following: when the grant goes away, the programs or innovations which it supported dwindle too. Don’t get me wrong: federal and foundation grants are essential for innovation to occur, and I have served productively on both the giving and receiving ends of this equation. But initial funding almost always ends at some point (through change of political priorities or foundation leadership and priorities), and additional funds must be sought for sustainability or going to scale.

I also ran a large urban hospital for four years. Among many other major lessons, I came to appreciate the beauty of funding formulas and streams that just keep on delivering resources every day or week or month. A good example are the Medicare payments supporting medical resident education; these “extra” payments are built into Medicare funding formulas and provide ongoing, regular support for this important activity. Of course, they certainly come under political scrutiny occasionally and have to be justified and defended, but by and large they are embedded as well-established fiscal supports like mortgage interest deductions or agricultural subsidies.

Despite more than a decade of helpful and creative public and private grants, I remain extremely concerned about our slow progress in addressing health inequities by geography, by race and by economic status. These injustices are sapping our national productivity and quality of life. We need to go beyond grants to identify sustainable resource flows that are up to the magnitude of the challenge.

I have been suggesting using some of the savings realized from eliminating ineffective and wasteful health care dollars, such as a share of ACO shared savings or IRS Community Benefit reform – but these may not be entirely workable or sufficient. We need to take a close and creative look at how to maximize efficiencies and possibly consolidate multi-sector revenue streams from education, business, and community development agencies toward achieving common health goals.

Voluntary efforts are not adequate if we are serious about this task. Let’s commit ourselves over the next decade to finding these more robust, dependable mechanisms and documenting real progress in the areas that will have the greatest impact on population health.

Resources for Population Health Improvement: What About the Savings from Waste in Health Care?

The release of the national County Health Rankings demonstrates how large the gaps are across our communities in both health outcomes and the factors producing health. Particularly in the lowest ranking counties in any state, there are significant resource constraints in all the factors producing health. These include health care access, disease prevention and health promotion programs and policies, early childhood and health literacy efforts, jobs and economic development, air and water quality, and the built environment. Where might the resources come from to make improvements toward better health—particularly for our most under-resourced communities?

One place to look is at the waste in our health care system. For years organizations as prominent as the Institute of Medicine have been observing that 25% to 33% of all of our health care expenditures may be wasted because they are ineffective in improving health. Dr. Elliott Fisher and colleagues at Dartmouth have been calling attention to the approximately threefold regional variation in per capita Medicare expenditures from $5300 to $16,300 without differences in health care quality and health outcomes (1). They have also recently observed inflation adjusted Medicare growth rates varying from 2.3% in Atlanta and Pittsburg to 5.3% in Dallas over the period 1992-2006. The Dartmouth group indicates that reducing the spending rate from the national average of 3.5% to the 2.3% experienced by San Francisco would save Medicare $1.3 trillion by 2023.

Looking at it another way, total health expenditures in 2009 were projected to be $2.5 trillion. If 25% of that could be saved, that would amount to $625 billion per year. Providing health insurance to everyone would require an estimated $100 billion per year. That would leave more than $500 billion for smoking cessation, exercise and nutrition efforts, education enhancements, job creation, and creating safer communities. To put this in context, total national expenditures in 2005-2006 for all K-12 education in the entire country was $461 billion.

However, achieving these savings is challenging. Republicans and Democrats alike are skeptical that cost-containing provisions of the Obama Health Insurance Reform law will be strong enough to reduce expenditures substantially. Even in places that have low health expenditures and good outcomes, cultures of cost effective practice have been developing for decades; they cannot develop overnight. A current proposal is to develop Accountable Care Organizations, in which financial incentives would be provided for developing efficient practices. Discussions have begun around the concept of shared savings, in which savings are divided between the providers who produce them and insurers. Vermont has already been using savings to hire staff for community clinical prevention while leaders in Minnesota have raised the possibility of using part of the savings to create Accountable Health Communities.

These are only initial ideas and beginning steps; vast resources will be necessary to fully implement the plans described above. Dan Fox, a careful observer of American health politics, has observed that policymakers “most likely would ration spending to improve overall population health in order to avoid rationing health care…there is no reason to expect that a value dividend, if one accrues, would be used for any other purposes than slowing the growth of spending or providing more access to health care (2).” Bentley similarly observes that “as a society we may prefer to provide care to the sickest, most vulnerable patients even though our money could buy greater improvements in life span or quality of life if used for another purpose (3).”

While common sense suggests a systemic streamlining that involves exchange of ineffective resources for those shown to be most (or at least more) effective, political realities are not necessarily rooted in common sense. Paul Starr’s book The Social Transformation of American Medicine sums up 150 years of medical history by saying that “the dream of reason did not take power into account.”

We must hope that approaches to shared savings in health care develop more robustly and gain traction. But they are not the only hope. We have to look for other inefficiencies as well. Governments, philanthropies, and businesses will have to make additional resources available. Promising current examples include the California Endowment’s Building Healthy Communities initiative and the Minnesota’s State Health Improvement Plan (SHIP).

In summary, waste and inefficiency in our health care system are one potential source for investing in the broader determinants of health. A fundamental population health challenge is to identify incentive structures and cross-sectoral allocation models to bring such possibilities into policy and practice.

A future post will expand on these ideas and examine additional sources of funding for fundamental population health improvement.

References:

1. Fisher ES, Bynum JP, Skinner JS. (2009). Slowing the growth of health care costs—lessons from regional variation. The New England Journal of Medicine, 360(9), 849-852.

2. Fox DM. (2010). Realizing and allocating savings from improving health care quality and efficiency. Prev Chronic Dis, 2010;7(5). In press.

3. Bentley TGK, Efros RM, Palar K, Keeler EB. (2008). Waste in the U.S. health care system: A conceptual framework. The Milbank Quarterly, 86(4), 629-659.

Where Would You Put the Money?

As regular readers know, I’ve been arguing consistently for the need for a regular, sustainable revenue stream to support population health improvement. However, I’ve not directly addressed the question of how these dollars should be allocated. As one of the authors of the important Evans-Stoddart population field model said in their 2003 AJPH article (Consuming Research, Producing Policy?), “redirecting resources means redirecting someone’s income…most students of population health cannot confidently answer the question…well, where would you put the money?”

Why is this so? Can’t we simply link the huge variation in health outcomes we see across states and communities to financial and non-financial policy investments over time? Why have we not simply estimated community level, per capita policy and programmatic investment in each health factor area (health behaviors, clinical care, social and economic factors, and the physical environment) to derive a base level of investment needed to achieve health benchmarks?

There has been some limited national and state level research and policy analysis on this question. The Trust for America’s Health (TFAH) estimated in 2008 that that investing $10 per person per year in proven community-based programs to increase physical activity, improve nutrition, and prevent smoking could save the country more than $16 billion annually within five years. In 2009, Kim and Jennings found that at the state level more generous education spending, progressive tax systems, and more lenient welfare program rules help to improve population health. However, the magnitudes of the effects were quite small, most likely because using the state as the unit of analysis masks much of the important variation in both outcomes and investments at more local levels.

We have suggested that since communities have different outcomes and determinants profiles, locally tailored “policy packages” might be an effective and efficient approach. These packages could be driven by the strength and breadth of local investments affecting the multiple determinants of health. Unfortunately, we are not aware of any national or state data sets that could inform development of such investment recommendations. The relevant financial data from the multiple local, state and federal public and private funding sources does not exist or is not standardized in ways to allow policy-relevant comparisons that would be useful to public and private policy makers. But I strongly believe that making real progress in this area will require that we systematize and standardize collection of these data.

In the meantime, we should rely heavily on currently available compass points. The County Health Rankings encourage comparison of health factor areas with national benchmarks. Everyone needs access to healthcare services, and the TFAH guidelines for prevention investments are useful. Many local public health departments are inadequately funded for their critical work. There is a strong evidence base that investing in early childhood and other education programs has efficient long term pay off in terms of health outcomes. And a strong argument can be made for resources to support the work of emerging multisectoral super-integrators that can play a critical role in identifying and harnessing resources.

We must start with what we have, by continuing to rely on existing tools and resources. But we must also move beyond these, to advocate for and insist on development of state-of-the-art surveillance systems to promote evidence-informed investment and stewardship of our limited and extremely valuable population health resources.

The broad population health model that underpins this blog and the County Health Rankings must have long-term relevance because quick fixes are few and far between. There is an imperative to invest now in factors we know to be strong drivers of long-term health, such as early childhood interventions (see Can We Afford to Wait for Better Evidence on Improving Child Health? and Business Investment in Early Childhood: Making Future Workers Happier). There is also a need for more research which can better estimate such long-term impacts; the IOM’s recent report on Public Health Measurement and Accountability calls for advancing the use of predictive and system-based simulation models to understand the health consequences of the underlying determinants of health.

One such model does exist, and was reported on in the May 2011 Health Affairs issue on Environmental Health discussed last week. The article, authored by Bobby Milstein, Jack Homer and colleagues, focuses on the HealthBound policy simulation model which has been developed by the CDC over the past decade. This work seems and is ambitious, as any such analytic and projection tool has to be, but it also simplifies the sheer complexity of the U.S. health system into a tractable form that can be understood and studied by diverse stakeholders. It is built on the methodology of systems dynamics modeling, using several hundred interacting elements and differential equations tied to ten national data bases and many key reference studies.

There have been various iterations of the model over time and several published reports, including an earlier overview in the American Journal of Public Health. The most recent Health Affairs article reported the results from simulating three strategies over 10 and 25 years to reduce deaths and improve the cost effectiveness of interventions:

expanding health insurance coverage
delivering better preventive and chronic care, and
protecting health by enabling healthier behavior and improving environmental conditions.

The main finding was that each would alone would save lives and provide economic value, but the combination of all three was likely to be more effective. For example, adding protection to the coverage and care scenarios would save 90% more lives and reduce costs by 30% in year 10, but by year 25 the protection investment could save 140% more lives and reduce costs by 62%.

Of course, what goes into the model determines what comes out, and it is difficult for someone not intimately familiar with the inputs and equations to evaluate strengths and weaknesses. Because of the complexity of all the multiple inputs and outcomes, precise estimates for every independent interaction over time do not exist, and ranges and sensitivity analyses are usually required. The authors have extensive experience with these cutting-edge methods; however they caution that “better data…would help narrow uncertainties and yield even stronger policy insights.” While the Health Affairs article placed more emphasis on health care elements with some behaviors and physical environmental elements in the protection scenarios, planners with more interest in these could simulate the “Pathways to Advantage” intervention, which summarizes intervention research around things like education, living wage, and job training.

While causal understanding of how such factors operate is perhaps more incomplete than those in the health care realm, the authors remain open to incorporating new findings as they emerge. They are also very interested in creating practical and relevant applications for policy makers, which is in part why a HealthBound game also exists to gain hands on familiarity with the methods and results.

The HealthBound tool is are not sufficiently precise to identify local opportunities to invest in specific programs and policies; however, other groups are moving the field in that direction. For example, the ReThink Health Initiative has begun to create simulation models and games that focus specifically on regional investments to transform health system performance. Policy makers need and want such guidance and these tools offer tremendous potential for population health policy – especially as they become increasingly robust and reliable over the coming decade.

For further reading, check out the Robert Wood Johnson Foundation’s interview with Bobby Milstein about his article on NewPublicHealth.org.

A Population Health Opportunity Map

A commentary in the May 25 JAMA by Jonathan Fielding and Steve Teutsch caught my eye a couple of months ago. Both authors play influential roles in national population health policy, from their practice based experiences in the Los Angeles County Department of Public Health.

They begin by arguing that as we reform the health CARE system, complementary improvements are needed in community prevention programs and policies as well. The challenge for both national and local policy makers is to be able to “identify and implement those interventions that provide the greatest benefits and value,” including the relative effectiveness of both clinical and community prevention efforts. They suggest that a first step has to be a sound framework for “identifying and organizing” the universe of interventions which have been shown to have evidence of effectiveness.

The components of such a framework are proposed to be: 1) the ecologic model, which is generally consistent with the multi-determinant population health model we advocate in this blog, 2) the life course perspective, in which health at any point in time is the “product of a person’s behaviors and exposures superimposed on his or her underlying biology,” and 3) the evidence of effectiveness of any intervention in either the clinical or community spheres with enough scientific evidence to support its use.

These three components make up their proposed model, called An Opportunity Map for Societal Investment in Health, pictured below.

The horizontal axis reflects the life course, from wellness through illness to death. The vertical axis reflects the spectrum of intervention strategies from the individual to the societal level. The blue shading of the bottom half highlights individual clinical investments while the brown at the top reflects community wide programs and policies. The blending of colors in the middle implies that these exist on a continuum. Another figure shows (using diabetes as an example) how this model can be applied to issue-specific clinical and community prevention efforts.

The authors explain that when “individual interests and needs predominate, there is a slide to the lower right quadrant, where costs of medical care are high and health status is poorest.” They draw the conclusion that “attention to interventions in the upper left quadrant may yield greater health and economic efficiency,” including individual productivity and national global competitiveness.

I believe that private and public policy makers faced with making the program and policy investments in a resource limited world are likely to find conceptual models such as this should be helpful. I would like to see a third dimension added to reflect the estimated cost-effectiveness of each program and policy choice, both clinical and societal. While most population health advocates would agree with the authors’ call for more resources in the upper left corner, each individual program or policy needs to be evaluated itself on some cost-effectiveness metric (such as quality adjusted life years gained per dollar invested). The addition of this dimension would reveal some very cost effective interventions in the lower right region of the figure and some very cost ineffective interventions in the upper left. In addition, attention must be given to relative cost effectiveness to improve health equity compared to improving overall population health; certain interventions will be more effective for one than for the other.

Will such broad “top down” conceptual models be helpful to local communities faced with making difficult decisions among dwindling resources? Next week I’ll try to address this question directly.

Do We Need Population Health Shock Therapy?

I just finished reading Thomas Friedman and Michael Mandelbaum’s new book That Used To Be Us: How America Fell Behind in the World It Invented and How We Can Come Back. It’s a sobering read as it assesses the U.S.’s position in the world and what we have to do to regain lost economic and social competitiveness.

Friedman and Mandelbaum focus on four areas we need to urgently address in order to avoid falling into second world status: globalization, the information technology revolution, deficits and debt, and energy demand and climate change. The authors express particular concern about our education performance, and argue that recent information about relative international test score performance should have prompted a response similar to the investments in science and technology spurred by Sputnik a generation ago. But that hasn’t happened.

I found their diagnosis compelling and sobering, so looked forward to reading their “prescription” in the final chapters. In a chapter called “Shock Therapy,” they argue that the current political paralysis is not up to these challenges and that the system calls for political shock therapy — a direct analogy to its once classic use in psychiatry — defined by the authors as a radical centrist third party.

What does this have to do with population health? I am basically an optimist, and I do believe that more attention is being paid to population health policy now than a decade ago. Friedman and Mandelbaum call themselves “frustrated optimists” – I, too, have days when I wonder whether we will be able to assemble the will and resources to show improvement in our health outcomes, including the persistent health disparities by race, education, income, geography, and gender.

In discussing this blog and the challenges of its having traction in the world of national policy debate, a wise colleague recently mused that that the intensity and frustration of current immediate challenges may numb many of us from even more distant and difficult goals. A recent New York Times op-ed argued that “problems like mass joblessness and starvation can seem so daunting that we stop trying to help.” Likewise, David Brooks recently commented on the limits of empathy, observing that “empathy has become a shortcut…a way to experience delicious moral emotions without confronting the weaknesses in our nature that prevent us from actually acting upon them.” He argued that religious, military, social or philosophic codes are much more powerful and are sources of identity and joy that trump empathy alone.

The radical centrist third party Friedman and Mandelbaum suggest for their four challenges would go beyond empathy and compassion alone and would likely have an impact on population health policy, particularly given its emphasis on education as well as science and technology. But how realistic is it, and do we sit by the sidelines for it to happen? My prescription for the next decade may not be shock therapy, but I hope that population health advocates would support the following steps until something more promising or radical emerges:

Be clear on our metrics for achievement and improvement, including disparity reduction.
Find savings from ineffective health care spending, and reallocate them to other population health determinants through such mechanisms as IRS Community Benefit reform and ACO shared savings.
Identify and enhance health promoting policies and programs in non-health care areas (Health in All Policies, Health Impact Assessments).
Identify cross-sectoral national and local partnership models with business models and financial teeth to leverage additional resources and policies.

Are there better goals for the next decade?

Two recent New York Times articles jumped off the page at me. The first, on the recommendation by the U.S. Preventive Services Task Force to forego routine screening for prostate cancer with the PSA test, received wide media coverage. The second, on an Institute of Medicine (IOM) panel recommendation that costs should explicitly be considered in deciding what benefits must be provided by insurance plans, received less attention. Both deserve attention from population health advocates and policy makers.

The reason for the PSA recommendation is that the best scientific evidence reviewed by the panel over several years shows that such routine screening does not save lives overall and “often leads to more tests and treatments that needlessly cause pain, impotence and incontinence in many.” Health care groups and patient advocates were quick to criticize the panel’s findings, in a similar pushback to the recommendation two years ago against routine mammography for women in their 40s.

While most of the PSA test media coverage has focused on effective care, we should also consider the panel’s recommendation from a cost-containment imperative. The fact is, resources are becoming increasingly limited and both Republican and Democratic policymakers (not necessarily office holders) agree that Medicare spending must be reduced to reduce debt – and, some argue, protect national security in the global economy. Some facts to consider:

As much as 25% of all health care expenditures are considered ineffective;
Miami spends twice as many Medicare dollars per person as Minneapolis but gets no better results;
We spend much more than any other nation on health care, with worse results.

There are two ways to achieve cost savings: provide fewer services and/or charge lower prices for each service. Any mention of this triggers loaded words from “rationing” to “government death panels.” I believe that while limiting services which have benefit is ethically and analytically challenging, eliminating those such as PSA screening with no benefit and even harm is not. But we must keep in mind that personal, professional, and political interests do not always align with the evidence: the New York Times article asserted that health reform legislation requires Medicare to pay for PSA screening regardless of the panel’s findings.

That the IOM committee should have to make a case for cost consideration in benefit design indicates how far from rationality we have strayed. I believe we can get back on track by agreeing that:

Cost containment is a national security priority;
We are wasting resources now;
We should channel our resources toward cost-effective investments in prevention and the social determinants of health (the Obama administration is very short-sighted in proposing $3.5 billion in cuts to the already modest Prevention and Public Health Fund);
We have opportunities to shift resources from ineffective health care to population health through community benefit reform and innovations from the Centers for Medicare and Medicaid Services (CMS).

We can’t have it both ways. We can’t lower costs without considering them. If evidence is not used to guide policy choices, what is the alternative? Perhaps we do need “shock therapy” to have evidence and economics drive our policy thinking. We can’t solve our health care and population health challenges without it.

In a previous post, I raised the question of who is accountable for population health outcomes, and suggested that some cross-sectoral integrating mechanism might be required. The outside line in the figure below suggests that this mechanism could be that of a Super-Health-Integrator. Such an integrator, with appropriate financial resources and authority, could align investments and activities across the multiple sectors which can impact population health, such as health care, public health, schools, employers, and community organizations.

An alternative to a super-integrator might be that one sector takes lead responsibility for population health improvement, using informal or formal authority to ensure that others play their roles. Regardless of which sector or organization took the lead (this could vary from community to community), the process would likely involve conflict and/or have limited effectiveness. Some concerns would be that healthcare organizations may overemphasize biomedical approaches, that governmental public health is too under-resourced for even its critical traditional functions, and that businesses would be challenged by competing goals. So, I believe that many communities would benefit by having a strong and neutral coordinating entity or mechanism at the helm.

Another alternative is the status quo where each sector makes investments to optimize its own goals, which may or may not include population health improvement. We have ample evidence to show that under this current situation few—if any—communities are as healthy as they could be.

This is why I proposed “health outcomes trusts” in my 1997 book and am proposing Super-Health-Integrators today. To my knowledge nothing like this has been fully developed, although pieces exist in many healthy community partnerships. Such an entity would likely not be governmental or corporate, but would certainly need active public and private sector involvement. And, as noted above, it would need some authority and financial resources to do its work (such as from a redesigned IRS Community Benefit stream). Such Integrators or Trusts might draw on the principles of social entrepreneurship by emphasizing strategic partnerships and leveraging resources to raise levels of performance and accountability (look for the essay by Jane Wei-Skillern in the upcoming issue of Preventing Chronic Disease, available online in mid-October).

I am not naive about the potential challenges such non-traditional structures pose, but the “inconvenient truth” is that addressing the multiple determinants of population health to optimize our communities’ health will almost certainly require some form of coordinating authority. I would love to hear the opinions of others on this point, as well as feature on this blog any examples of existing structures already performing these functions.

Locally Customized Population Health Policy Packages?

In my last post I suggested that those who allocate resources must provide ample guidance to ensure that local level health improvement strategies actually align with the best available evidence. I mentioned the University of Wisconsin What Works data base as well as the approach that the previous administration allocated its State Health Improvement Plan (SHIP) resources in the state of Minnesota. But I indicated that What Works is not tailored to individual communities and that the Minnesota example is limited to health behavior interventions, not all population health determinants.

We know from the County Health Rankings and our own experiences that communities vary widely in both their health outcomes and the factors or determinants of those outcomes. There are many examples of both high and low ranking counties which vary on their determinant profile…some have high health care quality and access but poor behaviors, others have high social factors like education and income but poor air and water quality. Given limited resources, it is critical that investments be made carefully to have the most impact.

Would it be helpful to identify a set of Population Health Policy Packages that suggest the best options for local communities to make, given their outcomes and health determinants profile? While there is enormous variation across the country in such profiles, it is likely that a reasonable number of representative situations exist for most communities/counties. For each profile, using the best evidence available from sources like What Works and the CDC Community Guide, a set of investment priorities would be developed, covering all the determinants of health. It would be as broad as the global evidence allows, but would be tailored to a community’s strengths and weaknesses. Options for improving behaviors like smoking would not be as highly suggested for places already doing well in this factor. The packages would not be prescriptive, but merely a menu of the investments likely to produce the best health outcome improvement. Where possible, options would include the strength of public and private sector policies beyond dollar investment in specific programs.

The initial set of Policy Packages would not be ideal, for a variety of reasons. We still have incomplete evidence of effectiveness of different programs and policies, particularly regarding cost-effectiveness beyond effectiveness itself. It is not clear which level of investment in a particular determinant or factor is optimal, or where diminishing return sets in and when resources should be moved to other factors. We are limited in evidence for different types of outcomes, particularly disparity reduction.

However, we shouldn’t let the perfect be the enemy of the good. A beginning set such as in the Minnesota SHIP example (i.e., improving nutrition, increasing physical activity, and reducing tobacco use and exposure) might be helpful in many places where discussions are taking place regarding improving the health of their communities. It would help ensure that local passion and commitment would be channeled in an evidence based direction, while preserving autonomy and sensitivity to community preferences.

What do you think about this? How long will we say we don’t have adequate evidence to guide population health investment decisions?

Can We Find Political Common Ground to Improve Population Health?

The next two months will be filled with harsh and divisive campaigning, deepening the ideological divide that characterizes our politics these days. Both conventions seemed primarily designed to energize their bases, by emphasizing the sharpest differences between the political “tribes.” Perhaps this is necessary in today’s politics, but it doesn’t bode well for population health policy over the coming decade. Improving population health will require cutting health care costs while preserving access and quality, enabling better health behaviors, improving education, economic growth, and the physical environment while also increasing social support and social capital. These are decisions that will require careful, nuanced decisions that go far beyond simplified political exchanges.

Last Labor Day I blogged on my summer read of Friedman and Mandelbaum’s book That Used To Be Us: How America Fell Behind in the World It Invented and How We Can Come Back, and their call for third party movement or even a new party which seeks to find common ground on such major challenges facing the country. This summer, I continued in this genre with Jonathan Haidt’s The Righteous Mind: Why Good People are Divided by Politics and Religion. Haidt is a social/moral psychologist, now at the NYU Stern School of Business. The book is a breathtaking synthesis of psychology, philosophy, evolutionary theory, anthropology, genetics, and political science. The book jacket poses these two questions: “Why can’t our political leaders work together as threats loom and problems mount? Why do people so readily assume the worst about the motives of their fellow citizens?”

Haidt sets out to answer these questions by dissecting what he calls our “moral intuition” (essentially our instantaneous perceptions of the world around us), arguing that our moral intuition operates much more quickly and strongly than rational thought processes. Through exhaustive psychological research, he identifies six moral foundations that he suggests characterize, in different proportions, global cultural and political “moral maps.” These include Care/Harm, Liberty/Oppression, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, and Sanctity/Degradation. (NOTE: for those interested in his ideas, these terms require a fuller elaboration than I have space for here because they are more nuanced than they appear at first glance).

He argues that these different foundations have a partial genetic basis, which can be modified by early development and later life experience. In addition, he asserts they have evolved in different societies and cultures to define a dominant moral intuition that he believes plays a powerful role in explaining our beliefs and ideologies. One prominent strand of argument is that while we are basically selfish, evolution does promote group interests to some extent.

With respect to American political culture, Haidt cites evidence, mostly from studying twins, that 30-50% of political attitudes have a genetic basis, with most differences between liberal and conservatives relating to sensitivity to threats and openness to new experience. His most relevant finding is that liberals bind together and primarily operate from the first three foundations above, while conservatives have a more balanced moral map or intuition across all six foundations. He argues that this produces a conservative advantage and explains why rural and working class voters often vote Republican: they are voting their moral interests which do not only focus on “the care of victims and the pursuit of social justice” as Democrats tend to but also include attention to Authority and Sanctity as well.

So what does this have to do with improving our health? If Haidt is fundamentally correct in his assertion that our political and ideological affiliations have a substantial genetic and evolutionary basis, and that liberals and conservatives differ in some of the dominant moral foundations from which they inherently operate, we had better understand those differences more fully if we are going to find ways to work together to address our nation’s challenges, including the many policies relevant to population health improvement. Does the goal of better health only address liberal moral foundations like Care and Fairness, or are there elements of conservative moral intuition that can help in finding common ground? I don’t yet have answers to these important questions, but look forward to others joining me in pondering this provocative area.