Embracing Complexity
in the process of identifying priority populations
Embracing Complexity in the Process of Identifying Priority Populations
What are Priority Populations?
Across industries and jurisdictions, many different words are used to describe what we refer to as priority populations. There is no singular term to encompass communities that may experience disproportionate impacts related to climate change and energy service delivery. Terms also have specific meanings in a variety of contexts. It is challenging to have different, nuanced definitions and to align them with organizational and policy objectives. The future of equitable service will require new modes of collaboration between utilities, communities, stakeholders, and government. Coordination around common terminology is essential in designing initiatives and directing funds toward the end goal of equitably serving all communities.
In this visualization, priority populations serves as a general term that encompasses the people or communities identified through the process detailed in this article.
Embracing Complexity in the Process of Identifying Priority Populations
Towards an Inclusive Definition
Communities are complex, ever-changing, and nuanced. People conceptualize and define communities in different ways.
At the same time, energy burden, negative public health effects, environmental pollution, impacts of climate change, and socioeconomic factors are unequally felt across communities – and some individuals and populations face these burdens more than others.
To better serve the needs of these communities moving forward—and address an often-long history of energy inequity—local, state, and federal government, utilities, and other entities are developing criteria to identify these priority populations. But what does this entail?
Embracing Complexity in the Process of Identifying Priority Populations
Untying the Knot
The process of identifying priority populations is complex and messy. Readily available data and geographic units of analysis don’t always align with the way most people think about communities, which can make identifying a group of people and/or a target location challenging. This process starts with a tangle of different definitions, data, and approaches. Here, ILLUME provides a pathway to untangle it.
1.Start by engaging stakeholders to understand the “why.” Before diving into the data, we step back and engage stakeholders, including local community and environmental justice (EJ) advocates, to gain a common understanding of why we are doing this work. Community advocates should be considered co-creators in the priority population identification process, from developing a definition to fine-tuning it over time.
2.Talk—and listen—to the stakeholders you’ve engaged to understand what “priority population” means to them. Embrace that this process will take time and requires authentic trust- and relationship-building. We also recognize that this is the most crucial part of the process, as our stakeholders help to reflect and articulate the perspectives of priority populations. These conversations serve as a touchstone throughout our process.
Once we have co-created a guiding vision of what priority populations means, it’s time for us to dig into the data and begin to ‘untie the knot.’
Embracing Complexity in the Process of Identifying Priority Populations
start to untie the knot
Click on the knots to explore each question. Click on the center to untie the knot.
What are the characteristics we use to represent priority populations – and how do we operationalize them?
The characteristics are the themes or concepts we use to broadly describe priority populations. Here, we embrace broad thinking, as we need to understand the ‘big picture’ before we identify the specific indicators (i.e., data points) we will use to identify populations.
What are the possible indicators, or data points, we could use to identify a priority population?
There are hundreds of unique indicators related to priority populations. Working with stakeholders, we document the full landscape of potential indicators we could use to represent the characteristics we co-identified. We organize the indicators by the characteristics we’re trying to measure. For example, we might use income (indicator) to identify regions of poverty (characteristic).
Embracing Complexity in the Process of Identifying Priority Populations
Click on the knots to explore each question. Click on the center to untie the knot.
What data exists—or doesn’t — for these indicators?
Once we have mapped out the landscape of possible indicators, we need to discover what data can feasibly be accessed. This stage helps clarify what is possible – and what isn’t. Then collect the data and prepare to dig in!
What is the relationship between priority population indicators?
Now it’s time to analyze the indicator data. We start by creating a plan to transform the raw data so it’s all on the same scale. Then we compare the indicator data – run correlations, look at means across regions, and map the data so we can see how it layers onto our geographic area of interest.
Embracing Complexity in the Process of Identifying Priority Populations
Click on the knots to explore each question. Click on the center to untie the knot.
Do the results of the scoring method we’ve selected match lived experiences?
Once we’ve examined the relationship between indicator data, we engage with our community stakeholders to walk through it together. We ground truth whether the data aligns with their knowledge of where priority populations exist in their communities. Paired with the data, these conversations will help us finalize the indicators and scoring methods used to determine priority populations.
What does the data tell us and how can it be combined or scored as our priority population criteria?
This is the stage where we determine how we will combine data from our different indicators to determine whether a community is a priority population or not. There are different options to choose from like using an index score and/or setting thresholds. Here, we work with our community stakeholders to tease out the pros and cons of different scoring methods. We embrace that this is an iterative process, and we will likely revisit several scoring options as we gain more perspectives and information.
Embracing Complexity in the Process of Identifying Priority Populations
Knots will persist.
As we untie some knots, others will form. Because we’re working with people and communities, it is critical to embrace the iterative nature of this process. We will revisit our criteria, and we will likely change it over time. That’s okay – in fact, due to the nuanced and ever-shifting needs and characteristics of individuals and communities, it’s critical to plan for change.
Some states are already modeling how this flexibility can look in practice. For example, in New York State, the Climate Leadership and Community Protection Act (CLCPA) stipulates that the Climate Justice Working Group (CWJG) will meet no less than annually to review the criteria and methods used to identify disadvantaged communities (DACs) and may modify methods to incorporate new data or scientific findings.
Embracing Complexity in the Process of Identifying Priority Populations
Knots will persist.
Knots are a natural part of the process of defining priority populations. In our experience, a few common knots emerge:
1
What is the “right” number of indicators to use?
There is no right or wrong answer, but it is crucial to understand the objective in defining a priority population. This will direct what types of indicators to include; working with community stakeholders to define that objective is a critical first step.
2
What is the geographic level of detail?
We might choose to focus the process at the census tract, block group, or some other level. We also must acknowledge that geographic boundaries do not always reflect the nuanced reality of communities, and work with those communities to define how to reflect that reality.
3
How do we access or identify data if it isn’t readily available?
Once we identify all the publicly-available data sources, it’s important to build in time to partner with entities that do have access to the data needed and have a plan in place to regularly refresh and update our criteria based on this data.
4
How do we identify and monitor the unintended consequences of the definition and how it is used?
There may be impacts from investment in priority populations, such as gentrification. We work with community stakeholders to name these possible unintended consequences, then track those considerations—and the potential implications—over time.
Embracing Complexity in the Process of Identifying Priority Populations
Visualizing
Opportunity
Maps serve as a foundational tool when defining priority populations. We use mapping and visualizations to help make decisions throughout this process – first to map indicators, then to map combinations of indicators, or factors. Sharing maps with EJ advocates can help ground truth the definition and aid in making critical decisions like thresholds for what is a priority population or the indicators to include in the definition.
Embracing Complexity in the Process of Identifying Priority Populations
Visualizing
Opportunity
In this section, we provide three example maps with areas that are urban, suburban, and rural. You can toggle the three factors—Health Disparities, Environmental Exposures, and Socioeconomic Vulnerabilities—to see which areas qualify as priority populations depending on the factors you choose to include in your definition.
There’s no right or wrong definition here and the choices will depend on the needs and uses of the definition. Toggling factors on and off and observing changes in certain regions allows communities to ground truth results. You can hone the right combination of factors for a definition through iterative toggling and ground truthing.
Health Disparities
Data includes asthma emergency room visits, premature deaths, and diabetes. Health disparities data can be particularly useful because these disparities are often a result of environmental pollution and poverty that are compounded when residents don’t have access to consistent healthcare.
Environmental Exposures
Data includes particulate matter, proximity to landfills, remediation sites, and proximity to waste combustors. Environmental exposures can be useful because these exposures can cause severe health issues and can be mapped using known geographical data.
Socioeconomic Vulnerabilities
Data includes the population below 80% of the area’s median income, percent below the federal poverty line, race, limited English proficiency, and historical redlining. Socioeconomic vulnerabilities are useful indicators because they identify potentially vulnerable residents who have limited ability to respond to climate crises.
Embracing Complexity in the Process of Identifying Priority Populations
This example uses simulated data to illustrate the changes to DAC
designation dependent on the factors selected.
While we used dummy data, the results reflect ILLUME’s experience
conducting this work. All data examples are hypothetical.
Visualizing Opportunity
Turn the toggles on and off to see how the factors impact rural, suburban and urban regions.
Urban
Suburban
Rural
Selecting only Health Disparities highlights areas with high
Health Disparities but with fewer Socioeconomic
Vulnerabilities and Environmental Exposures. In this
example, there is only one priority population in the urban
region. In comparison, if you select any other combination
of factors, you’ll see two or more urban priority
populations.
In general, we see priority populations across all three
regions. When diving into the data, we can see distinctions
regarding what type of health disparity is most prominent in
each region. For example, urban populations may face more
asthma emergency visits from living in a busy city with high
traffic.
Selecting Health Disparities and Environmental Exposures
pinpoints areas that score more highly in these two factors.
In this example case, this means no priority populations in
the rural region compared to when you also include
Socioeconomic vulnerabilities.
This means that the rural region could have less asthma
emergency room visits than the urban and suburban regions.
Additionally, their communities on average would not be as
close in proximity to waste or remediation sites.
Selecting only Socioeconomic Vulnerabilities brings the
focus to areas that are highly vulnerable to socioeconomic
factors with fewer health disparities and environmental
exposures. In this example, there are no suburban areas that
qualify as a priority population when only Socioeconomic
Vulnerabilities is selected.
What could be the reason? Well, it all links back to the
variables that comprise this factor. For example, the
suburban region may not face vulnerabilities such as being
low-income or having limited English proficiency – in
comparison to the urban and rural regions. Including
additional data variables in the Socioeconomic
Vulnerabilities factor—or swapping variables out for
others—could yield different results.
Selecting Health Disparities and Socioeconomic
Vulnerabilities identifies areas that score more highly in
these two factors and eliminates priority populations in the
suburban area.
When selecting only Socioeconomic Vulnerabilities, we see
that there are no priority populations identified in the
suburban region. This means that in addition to facing less
socioeconomic vulnerabilities, the suburban region also
could have less cases of diabetes and premature death in
comparison to the urban and rural regions.
Selecting only Environmental Exposures distinguishes areas
with pollution or other environmental exposures and fewer
Health Disparities and Socioeconomic Vulnerabilities. In
this example, there are no rural areas that qualify as a
priority population when only Environmental Exposures is
selected.
What could be the reason? Well, it all links back to the
variables that comprise this factor. As an example, the
rural region may not be as close in proximity to landfills,
remediation sites, or waste combustors – in comparison to
the urban and suburban regions. Including additional data
variables in the Environmental Exposure factor, or swapping
variables out for others, could yield different results.
Selecting Environmental Exposures and Socioeconomic
Vulnerabilities shifts the focus to areas that score more
highly in these two factors. This example includes one less
priority population than when Health Disparities is also
toggled on.
Our suburban region shows no priority populations
highlighted. As an example, the suburban region could have
less cases of diabetes and premature death in comparison to
the urban and rural regions, as well as less low-income
households.
Selecting Health Disparities, Environmental Exposures, and
Socioeconomic Vulnerabilities will result in priority
populations that have relatively high scores across all
three factors.
What is interesting to consider here is the relationship
among the three factors. Health Disparities, Socioeconomic
Vulnerabilities, and Environmental Exposures can have a
compounding effect, i.e., they can exacerbate one another.
Mapping all three factors together can show us the
communities that have multiple factors affecting them and
give further insight into the relationship among variables.
Embracing Complexity in the Process of Identifying Priority Populations
Think about your community.
You might be envisioning a city block, a small downtown, or the stretch of road that connects you to your neighbors. You might be picturing an apartment building, a cluster of single-family homes, or farmhouses surrounded by dairy barns and hay fields. Or you might be thinking about the neighbor you can count on for support in a pinch, the teacher who helped your child navigate through elementary school, the postal worker who always greets you on their mail run, or your friend who is always up for a walk.
Communities are centered around people. Without people, a community cannot exist. And people are complex, nuanced, and fluid. So, while we seek to use quantitative data to better understand and characterize communities, this type of exercise should always be grounded in human experience first. By leading with the perspectives of people’s lived experiences, one can begin to honor the many layers and complexities that are inherent in any given community.
Data tells one side of a story; ground truthing with community stakeholders and EJ advocates is critically important to ensure the human element is centered. By integrating authentic stakeholder engagement with a data-driven solution, priority populations are better identified. People are at the heart of every community – and they should be at the heart of every policy and solution.