Improving Population Health

Population Health: If It's Everything, Is It Nothing?

By David A. Kindig, MD, PhD

A respected colleague recently emailed a response to the post on advance directives that “it could imply that everything is about population health (which, as you and I agree, I hope) doesn’t do much to advance the cause of purchasing population health.”

Hmmm…if it’s everything, is it nothing? A look at the population health model underpinning this blog and all I work on in population health, suggests that indeed it is perhaps everything: a broad set of outcomes produced by a comprehensive set of determinants which are influenced or activated by programs and policies in the public and private sectors.

Doing Well or Doing Better

By David A. Kindig, MD, PhD

When it comes to population health, is being the best the only thing we want to measure and reward? What about communities and states that are improving most rapidly, particularly from poor baselines? This issue is front and center in education policy: should we reward or incentivize schools or teachers who have the best student test scores, or those demonstrating greatest improvement?

I think both approaches are useful. Overall achievement should be measured and recognized, as with Olympic medals. But there’s also a case to be made for keeping track of improvement, particularly for purposes of allocating resources to communities with formidable social and economic challenges.

"If Not Genetics, Then What?"

By David A. Kindig, MD, PhD

“If Not Genetics, Then What?” is the title of Chapter 6 in the important 1994 population health book Why Are Some People Healthy and Others Not: The Determinants of Health of Populations. Canadian population health geneticist Patricia Baird contributed a chapter on “The Role of Genetics in Population Health,” which concludes as follows: “There are some disorders where the inherited metabolic machinery of the individual will not allow normal functioning in the usual range of environments; the associated diseases will usually burden the earlier part of the human life cycle. These provide a real and appropriate place for genetic service programs to contribute to population health. The complexities of the web of causation for most common afflictions in adult life are likely to limit the contribution of DNA identification of at risk genotypes. The potential for wasting resources and causing harm is real and serious.”

"Where Would You Put the Money?"

By David A. Kindig, MD, PhD

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?” 

IOM: Measurement for Accountability in Public Health

By David A. Kindig, MD, PhD

I was privileged to appear last week before the Institute of Medicine (IOM) Committee on Public Health Strategies to Improve Health. The purpose of the meeting was to gather testimony for its third report (due out later this year), which will develop recommendations for funding state and local public health systems after health reform. 

In preparation for my presentation, I reviewed the first report in this series, released December 2010, called For the Public's Health: The Role of Measurement in Action and Accountability (the second report on statutes and regulations is currently under IOM review). The report has three main chapters, including: (1) Needed: An information Enterprise to Drive Knowledge and Population Health Improvement, (2) Measuring Health for Improved Decisions and Performance, and (3) Measurement and Accountability. Given the report’s strong population health message, I am focusing this post on a few of its key findings and recommendations.

Population Health Disparities: Rates or Burden?

 

By David A. Kindig, MD, PhD

In my March 14 post Bending Health Disparity Curves, I focused exclusively on differences in mortality rates, such as deaths per 100,000 persons. Rates are very useful measures, because they allow comparison across populations of different sizes. But from a population health perspective, rates alone are not enough, because large disparities in very small populations have a different impact than similar disparities in larger populations. Burden refers to the impact of a health problem in a population, combining both the rate and the number of people affected.

Although our disparities focus tends to be on race and ethnicity, disparities also exist in other domains such as geography, socioeconomic status, and gender. The table below shows a surprisingly high male mortality rate, but it is the size of this population (146 million) that transforms the rate into a significant population health burden.

                               Mortality rate per 100,000*         Population Size

Black                      1009                                           39 million

White                      780                                             240 million

Male                        945                                            146 million

Female                    672                                            150 million

*Average rate 2003-2007. Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File 1999-2007. CDC WONDER On-line Database, compiled from Compressed Mortality File 1999-2007 Series 20 No. 2M, 2010.

Disparities Dialogue

By David A. Kindig, MD, PhD

Over the past couple of months, I’ve given some attention to the issue of health disparities by asking how we should focus our efforts and giving a perspective on bending health disparity curves.

I always appreciate when readers respond, whether to agree, disagree, or offer an alternative perspective and I’d like to share a couple of comments on the disparities posts that expand on my ideas.

Bending Health Disparity Curves

By David A. Kindig, MD, PhD

I recently raised the issue of disparities and noted the many unacceptable health differentials that exist across  U.S. subpopulations. It is very easy to claim that such disparities should be eliminated, but seldom do we set specific quantitative targets for such improvement.  

These days, the phrase “bending the curve” usually applies to reducing rates of health care cost increases. But look below at two other curves -- disparities in US mortality between Blacks and Whites as well as males and females from 1979 to 2007.

Which Health Disparities Do We Want to Reduce?

By David A. Kindig, MD, PhD

Over the past few weeks, I have been blogging on population health measurement, under the headline that you can’t manage what you can’t measure. While I’ve given quite a bit of attention to overall outcomes, I haven’t directly addressed disparities.

Improving the length and/or quality of life overall doesn’t necessarily translate to improved health for all population groups. Policies and programs can have a differential affect on subpopulations that exacerbate rather than diminish disparities.

Deciding which outcomes to target to improve overall health involves value judgments – whether the goal is improving overall health or reducing disparities. There is not a “correct” formula for simultaneously improving overall health and reducing disparities. The 50:50 weighting suggested by the figure below represents just one possible approach but the ethical dimensions of such choices cannot be ignored; in a resource limited world, should we focus on raising the bar overall or narrowing the gaps?

Doing Well or Doing Better

By David A. Kindig, MD, PhD

Our focus for the past couple of weeks has been on outcomes, both long-term and intermediate. Most of the examples mentioned have been discussed in the context of absolute achievement, such as being #1 on state or county rankings, or achieving the lowest infant mortality rate among all nations.

But is being the best the only thing we want to measure and reward? What about communities and states that are improving most rapidly, particularly from poor baselines?  This issue is front and center in education policy: should we reward or incentivize schools or teachers who have the best student test scores, or those demonstrating greatest improvement?

Is Chronic Disease Burden a Population Health Outcome?

By David A. Kindig, MD, PhD

Responding to last week’s post on which outcomes we should improve, Matt Stiefel from Kaiser Permanente asked about using the incidence or prevalence of chronic illness as a population health outcome measure. Matt’s important question prompts me to continue the conversation this week.

The Institute of Medicine’s 2008 State of the USA Health (SUSA) Indicators report (I served on this committee), called for an index of chronic disease prevalence among its seven other health outcome measures that “reflect the overall health of the nation and the efficiency and efficacy of U.S. health systems” such as life expectancy at birth, infant mortality, and  unhealthy days (physical and mental). To my knowledge, such an index has not yet been developed. As a member of the IOM SUSA committee, I am concerned with the classification of chronic disease prevalence (and other similar  factors) as outcomes per se, because improving these factors is the means, not the ultimate end that we seek (i.e., living longer healthier lives). This opens the door for the means to become the end.

Which Outcomes Should We Improve?

By David A. Kindig, MD, PhD

I recently argued on the importance of keeping population health outcomes and determinants separate, since the former represents today’s health and the latter tomorrows’ health. This week and next, I’ll be digging a bit deeper to look first at outcomes and then later at determinants.

Health outcomes have been defined as “all the possible results that may stem from exposure to a causal factor from preventive or therapeutic interventions.” As Gib Parrish noted in his 2010 award-winning article, there are many different ways to measure health outcomes. These include:

• Life expectancy from birth 
• Age-adjusted or age –specific mortality rates
• Condition-specific changes in life expectancy and mortality rates
• Self-reports such as general level of health

Parrish notes that outcome metrics should present both the overall level of health of a population and the distribution of health among different geographic, economic, and demographic groups in the population.

The MATCH population health model underpinning this blog reflects much of this perspective. The 2X2 outcome diagram below includes both mortality and non-mortality (i.e., health related quality of life) components, as well as both population mean and population disparity metrics. This makes sense conceptually, but poses issues and choices in practice.

 

Assessing Today's Health...and Tomorrow's

By David A. Kindig, MD, PhD

Today I’m starting a new series on population health measurement, motivated by the idea that “you can’t manage what you can’t measure.”  While there is no shortage of measures and rankings, there’s actually little consensus on what metrics are best suited to assess progress in population health improvement. Last week I urged Triple Aim architects to clarify how population health is defined in their model, but they’re not the only ones with definitional issues.

Metrics can assess a single item, such as infant mortality rates, or be bundled into groups to create summary measures. America’s Health Rankings (AHR) and the County Health Rankings (CHR) both use summary measures to annually rank, respectively, the health of states and counties within states. AHR has been ranking the health of states since 1990 and released its most recent rankings in December 2010. This important national resource informed work by the University of Wisconsin Population Health Institute to rank the health of Wisconsin counties beginning in 2003 and, for the first time in 2010, counties in every state. Dr. Pat Remington and I are proud to serve on AHR’s Scientific Advisory Committee.

The AHR and CHR share a common goal of population health improvement and both projects take pride in their deliberate, systematic, and transparent approaches. Both projects create summary measures for health outcomes (using metrics such as premature death, quality of life, and poor birth outcomes) and health factors or determinants (using metrics that capture health behaviors, clinical care, and social/economic/physical/policy environments). However, while CHR uses the health outcomes summary measure to identify the healthiest counties, AHR creates its overall rankings based on both outcomes and determinants.

So what does this look like in action? From 2009 to 2010, Wisconsin dropped from 11 to 18 in AHR overall rankings. But, a look at separate outcomes and determinants rankings over the same time period tells a slightly different story. Our determinants ranking is identical to the overall ranking (we dropped from 11 to 18). However, our health outcomes ranking improved slightly, from 16 to 15.

Why is this important? In general, we think of outcomes as a reflection of our current health and determinants as a predictor of our future health. The two summary measures often move in the same direction, but not always. When we launched the national CHR in 2010 with support from the Robert Wood Johnson Foundation, some wanted to know why our rankings didn’t have an overall score like AHR. The answer has to do with how the results are communicated and their implications for action. We felt strongly in the value of the two different measures to communicate different but equally important concepts about population health, particularly for policy makers.

Places with determinants better than outcomes are on the right track toward health improvement. But, for those such as Wisconsin with declining determinants, unless we act to reverse the trend, there is cause for concern about potential declines in health outcomes. It is the outcomes we are trying to improve, and the determinants that will get us there.

David A. Kindig, MD, PhD, is Emeritus Professor of Population Health Sciences and Emeritus Vice-Chancellor for Health Sciences at the University of Wisconsin School of Medicine and Public Health.

Population Health Targets: Unpopular But Needed

By David A. Kindig, MD, PhD

Thinking about the upcoming release of the federal Healthy People 2020 (HP2020) report has brought me back to a 2008 JAMA commentary I wrote with Yukiko Asada and Bridget Booske titled “A Population Health Framework for Setting National and State Health Goals.” Our recommendation was for a population health model framework to guide federal and state planning processes. Specifically, we called for the setting of five to ten year targets for broad outcomes. We suggested that the targets could be established based on trend comparisons across other nations or states and be set at different levels, e.g., ranging from “minimal” to “achievable” to “challenging.”

It is not clear at the moment if HP2020 or similar plans in individual states will include targets for broad health outcomes, such as years of healthy life or overall disparity reductions. In the past, targets have been set for specific narrower objectives such as cancer deaths and smoking rates. I think it’s time to also focus on the broader comprehensive measures of health that these narrower objectives produce.

There are both political and practical reasons why governments resist such explicit projections. As Smith and Busse say in a recent MATCH essay on the European target setting experience, one purpose of target setting is to “enhance the accountability of government to parliament and the electorate,” but such future accountability may not be politically advantageous. Even in a politically progressive state like Wisconsin, neither Republican or Democratic elected officials or appointed public health leaders have set such targets; this is one reason our University of Wisconsin Population Health Institute initially developed the Wisconsin County Health Rankings in 2003 and issued the first Health of Wisconsin  Report Card in 2007 in which we graded the state a “B-“ for overall health and “D” for health disparities. While these reports have been widely used, the recent Healthiest Wisconsin 2020 State Health Plan does not include broad 5-10 year targets.

My management experience has taught me that you can’t manage what you can’t measure. Since improving broad population health outcomes requires the actions of multiple sectors, Smith and Busse identify a need for targets that transcend traditional sector boundaries and emphasize “coordination, persuasion, and engagement if they are to be successful.” Of course, long term targets cannot be set precisely, but a set of minimal to challenging goals could be devised. They could be used by those community entities -- such as  United Way, a local or regional health department, or a Triple Aim project -- which aspire to provide the integration that population health improvement requires. Targets should help shape community health improvement processes by helping identify local health priorities as well as corresponding sets of cost-effective policies and programs.

We can and should set reasonable, broad targets for mortality and morbidity improvement and disparity reduction at the federal,  state, and community levels. These will guide investment, inform public accountability—and hopefully improve population health.

David A. Kindig, MD, PhD is Emeritus Professor of Population Health Sciences and Emeritus Vice-Chancellor for Health Sciences at the University of Wisconsin School of Medicine and Public Health.

How Healthy is Your Community?

By Patrick Remington, MD, MPH

Nearly everyone would like to know the answer to the question, “How healthy is your community?” One way this question is being answered is with the Robert Wood Johnson funded County Health Rankings which compare and contrast the health of over 3,000 counties in the 50 States nationwide, providing for the first time a standard way to measure the overall health of nearly every community in the nation.

The measures used in these Rankings are similar to those that have been used to rank the health of counties in Wisconsin over the past seven years. Last year, as we started the process of developing the national County Health Rankings, we contacted experts in population health measurement and asked them to review the best approaches to measuring health the health of communities. They each reported their findings in reports that have just been published in the CDC’s online journal, Preventing Chronic Disease.

These papers are the first of three collections of essays that will be published in the journal to cover a wide range of population health measurement issues corresponding to our model of population health. Upcoming issues will focus on incentives and partnerships for population health improvement (in September and November respectively).

Papers in the current issue on metrics consider best ways to measure the health outcomes of populations including disparities; factors that contribute to health including clinical care; health behaviors; social and economic factors; the environment; and programs and policies that can be implemented at the population level.These essays serve as an important bridge between the world of population health research and people who want to know how healthy their community is. Of course, there is no perfect way to measure the health of populations, just as there is no perfect way to measure the health of an individual.

Clearly, data exist today that can be used to measure the health of the populations. However, it requires that data be collected along the continuum from individuals, to families, organizations, communities, and the broad environment. This calls for using multiple sources of data in each community. Information about individuals is sometimes collected from the entire population, or from a sample, such as through telephone surveys.

There are countless indicators that can be used to summarize the health of populations. For the County Health Rankings we chose five measures of health outcomes and 23 measures of health factors that were available in nearly all of the 3,000 counties in the U.S. However, communities that have access to more data may want to dig deeper and explore additional measures of population health. In addition, different metrics are needed to measure improvement in the short to medium run than are used to measure current achievement as is done in the County Health Rankings.

So how healthy is your community? Visit the County Health Rankings to get one view. Then read these essays to learn more about ways to measure the health of population. Contact us if you have ideas about better ways to measure the health of your community. And stay tuned for the two more sets of essays in the September and November issues of Preventing Chronic Disease which will deal with the issues of incentives and partnerships for population health improvement.

Patrick Remington, MD, MPH is Associate Dean for Public Health and Professor of Population Health Sciences at the University of Wisconsin School of Medicine and Public Health.