Population health has been defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group.” Measuring population health and its distribution can unite groups across sectors around a set of clear, defined goals. However, no one metric can capture the intricate and complex nature of population health. Instead, we need a matrix of indicators to gain a full picture of health and how it is changing. (see Figure 1). For example, rather than measuring only end-of-the-line health outcomes such as mortality, we need to measure a range of metrics across the health pathway, including the determinants of health, risk factors, prevention and treatment.
In addition, to understand the distribution of health in a population and address inequalities, we need to measure health across different subpopulations.
Yet there is little evidence on which sub-population groups should be considered. Commonly used segmentations are based on socioeconomic status, geography, gender and ethnicity. However, population health can also be explored across different disease or age groups. In addition, risk factors play an important part in determining population health, and could provide a basis to segment the population. There also exist specific societal or clinical groups that carry particular relevance to policymakers, such as employees, prisoners, homeless people, disabled people or people with drug dependencies.
While all these population groups are important to population health, it is not practically possible, or desirable, to measure and present health outcomes across all possible dimensions. Therefore, we conducted an expert Delphi study, which uses several rounds of questionnaires, where the results from earlier rounds feed into the next in order to reach a consensus among participants. Our goal was to prioritize population segmentation approaches, and guide both the collection and presentation of population health data.
Implications For Policymakers
There exists a clear consensus among health care experts around the need for population segmentation in order to measure population health and health equity. However, there is no single way to do this. All ten population segmentation approaches were considered important by the panel. These results highlight the value of considering the wide range of different population groups that may influence health outcomes.
The results of this study can help researchers and policymakers prioritize the way they analyze and present population health data. In addition, these results should guide the collection of data. For example, the panel considered socioeconomic status and risk factors to be very important, but administrative datasets collect information on these issues in different ways and according to different definitions. Standardizing the collection of segmentation variables would allow population-wide analysis of the distribution of health.
Policymakers should also consider using a data-driven approach to identify population segments, rather than a priori defined population groups. Big data and data mining techniques can help quantify the distribution of outcomes in a population and identify the factors driving these differences.
It is important to note that measuring the distribution of health is only one of the many steps we can and should take to create healthier populations. We must continue to explore how population health will benefit from emerging innovations in technology, service model design and big data and analytics.