In establishing a framework for linking performance incentives to population health metrics, researchers must answer multiple questions.
Are the measures actionable? If so, at what level and by whom? Although these articles focus on community-level interventions, not all the suggested metrics seem to be actionable at that level. Nor would they necessarily be applicable for the range of organizations and agencies that affect population health in communities. A related question is whether all metrics should be actionable. Some of the suggested metrics — such as those in the socioeconomic domain — are contextual variables that influence health status and health care access and use and should be taken into account in assessing community-level performance. Such metrics may be actionable at the state or national levels, rather than the community level.
Are the measures sensitive to interventions?
If so, within what time frame? A system for rewarding initiatives to improve population health needs metrics that not only respond to interventions but also do so in a realistic time frame for incentives to be meaningful. As the population health outcomes article points out, for example, life expectancy and age-adjusted mortality are measures of population health that are amenable to intervention, but not necessarily in a realistic time frame (6
). Also important is whether metrics are sensitive to interventions at different levels: upstream, midstream, and downstream. Those terms may have different meanings in different contexts and domains. The authors of the public health policy article (1
), for example, describe upstream approaches as those with the potential to affect large populations through regulation, increased access, or economic incentives. They classify interventions in organizations, such as worksite health improvement programs, as midstream, and individual-level behavioral approaches as downstream. The environmental metrics article (5
) contrasts environmental factors, such as air quality, that affect human health directly and proximately with upstream factors, such as a community's energy sources, that affect health indirectly. In the social and economic determinants article (3
), upstream refers to the social determinants of health.
Are the measures affected by population migration? This question is of particular relevance for analyzing community-level health metrics, especially longer-term, because the composition of local populations can change substantially. Changes in life expectancy over time at the community level, for example, will reflect changes in population composition as well as changes in underlying health status.
Are the measures easily understood by collaborating organizations, policy makers, and the public?
The need for simplicity and easy comprehension is a common theme in several of the articles (1
). When complex measures — such as the univariate inequalities measure, which assesses overall inequality across a population, regardless of association with other attributes (7
) — are proposed, one question that arises is whether an effective communications strategy could facilitate understanding. Although metrics linking workforce health status and productivity have been established, the business case for addressing the health of communities may be less clear (8
Is the meaning of an increase or decrease in a measure unambiguous? For most of the suggested measures in the articles, a change in a given direction can be readily interpreted as positive or negative. For some measures, however, the implications of a change in a particular direction may be unclear. In the case of participation in social welfare programs, for example, higher participation rates may reflect increased economic hardship in a community (negative), more effective outreach to the low-income population or more generous eligibility criteria (positive), or both.
Do the measures stand alone or are they aggregated into an index or summary measure?
The articles differ in the extent to which they recommend aggregation. The outcomes and inequalities articles (6
) promote the use of summary measures — exclusively in the case of inequalities — and the socioeconomic determinants article (3
) suggests the possibility of using an index or identifying complex measures by using factor or principal component analyses. A major advantage of a summary measure is parsimony; having a large number of metrics can lead to loss of focus, which a single measure avoids. In the case of a weighted measure, however, reaching agreement on the appropriate weights may be difficult and ultimately subjective. Several of the previous questions, moreover, have particular bearing on these more complex types of measures. Is their meaning clear to users? Are they readily actionable? Are they responsive to interventions? Does a change in a given direction have an unambiguous interpretation? The answers to those questions depend in part on whether a complex measure can be disaggregated into meaningful components. In that regard, the inequalities article (7
) provides an example of how to isolate the contributions of different attributes to an overall measure of inequality, thereby guiding intervention priorities.
Are the measures uniform across communities?
Although measures need to be comparable across communities, some flexibility may be necessary. In the case of health determinants, the particular domain is pertinent. One could make a case for standard measures of behavioral risks, for example, because such risks are not community-specific. However, environmental issues vary widely among communities, leading those authors to suggest that communities should be involved in both defining and using environmental metrics (5
). A possible approach, at least for some domains, is to have a core set of standard measures, with additional measures selected by the community.
To what extent do measures address disparities as well as overall burden?
The articles adopt different perspectives toward disparities. The health care article (2
) proposes a single measure to track disparities, whereas others (1
) suggest that the ability to identify and monitor disparities should be an integral feature of all measures. However, the health policy article (1
) points out how disparities assessment is limited in that domain. Notably, most of the articles assume a bivariate approach to disparities measurement rather than the univariate approach that the inequalities article (7
Can unintended consequences be tracked? None of the articles mentions the potential for unintended consequences that may result from the use of certain metrics in an accountability-based system — an issue that has arisen in the clinical setting. If incentives reward improvements in specific population health measures, tracking additional metrics may be necessary to ensure that any improvements do not come at the cost of deterioration in other population health domains.