How to Best Use Benchmarks when Exploring Population Health Data

It is well established that assessing a community before developing and prioritizing strategies to advance community well-being is important. This process often involves exploring qualitative data (e.g. community listening sessions) and quantitative data (e.g. population health indicators) —often referred to as Community Health Assessment, or Community Needs Assessment. These assessments are important because they help changemakers account for local community conditions when planning to improve the community, rather than generalizing or making assumptions about what is needed. When exploring quantitative data about a given community, benchmarks serve as helpful comparative references—added context through benchmarking tells us more about how a community is really doing.


Benchmarks help us interpret population health data

Benchmarks serve as points of reference and give context to help us interpret what we see in the data. Without benchmarks, it can be difficult to draw conclusions from data. What can we make of the fact that the rate of infant deaths per 1,000 live births is 5.87 in San Bernardino County, for example, without a benchmark for comparison?

Without benchmarks, it’s difficult to know where to focus efforts and how to prioritize investments. We can always work toward improvement, but strategic plans and collaborative efforts are often met with scarce resources—benchmarks provide context for what we’re seeing, so we can make better strategic decisions about where to act and invest together.


When considering how to use benchmarks for comparative reference in a given community, there are a number of factors to consider:


1. Sourcing relevant data: In order to consider a particular benchmark, one must access comparable data for the benchmark area. For example, in order to compare San Bernardino County, California to the state as a whole, one must access data for the entire state. If one wishes to make a national comparison, the same data must be available across the U.S. In short, before selecting benchmark(s), it’s important to ensure data availability.


2. Selecting an appropriate benchmark: In order to identify areas of concern or priority for action in a community, it’s helpful to consider geographic context. For example, one would expect the rate of infant deaths in most of California to be better than the national average because California as a state typically performs better than much of the rest of the U.S. on population health indicators. Therefore, choosing a national benchmark would not be as meaningful as a state or peer community.


3. Using more than one benchmark: The more context one has for interpreting data, the more meaning one can glean, so when possible use multiple benchmarks to triangulate findings. When dealing with population health data, state and national averages are often used as benchmarks, but there are others that might work well for your community and project. It often makes sense to compare data from two neighboring communities, or even two communities that aren’t contiguous but that have similar populations, demographics, and community conditions. Similarly, regional benchmarks can offer additional nuance. One might expect data from two similar communities to be similar, so identifying specific areas where one community fairs better or worse than the other could shed light on “low hanging fruit” strategies for community improvement. Other commonly-used benchmarks include well-established goals, like Healthy People 2030 or the UN Sustainable Development Goals.


Example: Using benchmarks to explore data in San Bernardino County

In San Bernardino County, California, a group of stakeholders is examining data around maternal and infant health to determine how the county fairs today, and what might be done to improve maternal and infant health in the region. One key indicator is Early Prenatal Care (the percent of births for which prenatal care began in the first trimester), a dataset made available through CDC WONDER. Early prenatal care is important because it reduces risk of pregnancy and birth complications and is associated with better maternal and infant health outcomes. In San Bernardino County, the percent of births with early prenatal care is 84.2%, which is higher than the national average (benchmark) of 77.6%, lower than the California state average (benchmark) of 85.5%, and just slightly lower than the neighboring (peer) community of Riverside County.

As you can see, examining benchmarks alongside data for San Bernardino County provides a more complete picture of how the community fairs when it comes to early prenatal care. We can infer that San Bernardino County provides better access to prenatal care than most communities in the U.S., but that there is still room for improvement compared to the rest of the state, and thus increasing early prenatal care as a means to improve maternal and infant health is likely worth prioritizing in San Bernardino County.


Key considerations when using data benchmarks

1. Benchmarks can serve as goals but should not be confused with goals—just because your community is faring better than the state or nation when it comes to the rate of infant deaths, for example, doesn’t mean there isn’t still need for improvement. Benchmarks provide useful context to aid in interpreting population-level data, whereas goals are established objectives that, when reached, indicate a level of success in improving population health. Ideally, population health goals are established and worked towards collectively—among stakeholders, community partners and organizations, and residents.


2. Benchmarks do not always shed light on disparities. Where possible, it is best to examine data (and associated benchmarks) broken out by race, ethnicity, and other population groups. See the example below.


Overall, San Bernardino County is performing about the same as the national benchmark when it comes to infant deaths (the number of deaths among infants (less than one year of age) per 1,000 live births). However, when we explore data by race and ethnicity (see graph below), it’s clear that the rate of infant deaths is almost twice as high among Black and African Americans—clearly there is much room for improvement when it comes to maternal and infant health. Looking at the data broken out by population groups exposes important opportunities to support specific priority populations to decrease disparities and infant deaths overall.

3. A limitation to using state and national benchmarks is that when we lack data for some geographies we aren't using a truly representative benchmark. This is relevant for datasets for which national and/or state data may be incomplete, or spotty.


How benchmarking is used on IP3 | Assess

IP3 | Assess is our web-based solution to community health assessment, and for most indicators on the platform, it provides access to both state and national benchmarks for comparison. The output on the Frameworks screen (see below) uses a z-score analysis and the fuel gauge visualization to provide a quick snapshot of how a given community fairs relative to either the state or national benchmark—green indicates the community fairs better than the benchmark, and red indicates the community fairs worse than the benchmark. In this way, use of benchmarks equips IP3 | Assess users to quickly identify areas to prioritize in terms of improvement and investment. We can also create custom benchmarks for IP3 | Assess Enterprise users that are especially relevant to their community(s), like an aggregate of all of a hospital’s service areas.


If you're interested in learning more about IP3 | Assess to gain access to and explore data relative to benchmarks in your community, please get in touch!



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