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?

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.

From a statistical perspective, measuring improvement is even more difficult than measuring achievement. For our County Health Rankings, we average three consecutive years of data to assess premature death as Years of Potential Life Lost (YPLL) before age 75 while for other measures, such as smoking rates, we use up to seven years of data. Using multiple years of data allows us to measure health in nearly every county in the US.  But the need to use multiple years of data to get an estimate for a single point in time makes it difficult to measure change over time, particularly for smaller communities that have greater variability in their measures.  

We should not let these analytic obstacles deter us from incentivizing doing better at the same time we honor doing well. On the data side, we need to oversample rural areas and seek out non-traditional data sources such as healthcare, employer and school records (with adequate individual privacy protection of course). For measuring improvement, we should focus attention on shorter-term metrics (such as rates of infant low birth weight) and closely track indicators that are strongly correlated with health outcomes (such as burden of chronic disease, income, and education). And we should experiment with both quantitative and qualitative metrics for assessing innovative and emerging intervention strategies.

Since no community has achieved the highest health outcomes that it can, each can aspire to do better and even be the best. The National Priorities Partnership of the National Quality Forum has set one goal that “the health of American communities will be improved according to a national index of health.” But to reduce inequalities across communities (a topic I’ll return to soon), those with worse outcomes need to improve at a faster rate to reduce the gap. As with our schools, learning what combination of metrics, mechanisms, and incentives maximize rates of community health improvement will be an important part of population health policy strategy in the coming decades.

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.