The Oregon Tobacco Prevention and Education Program (TPEP) funded the development of this data equity framework. The information on this website was adapted from a report created by Rede Group on behalf of the Oregon Health Authority’s Public Health Division (OHA‑PHD). We recognize and appreciate the OHA staff and the community‑based organizations (CBOs) who served on the Data Equity Advisory Board (DEAB) and helped co‑create this framework. The participation and insight of TPEP’s CBO partners were essential to completing this work.
Table of Contents
Executive Summary
Background
In 2023–24, the Oregon Health Authority (OHA) hired Rede Group to support the work of the Data Equity Advisory Board (DEAB). The goal of this partnership was to help create a data equity framework for the Tobacco Prevention and Education Program (TPEP).
The DEAB included 13 community‑based organizations (CBOs) that receive TPEP funding. The group met regularly over five months, from September 2023 through January 2024.
During this time, the DEAB worked together to identify what data communities need, where gaps exist, and how data can be more accessible and useful. They also explored ways for communities to have more ownership and involvement in how data is collected, shared, and reported. In addition, the group identified what resources, training, and technical support CBOs need to participate fully in data work.
This process helped shape a stronger, more community‑centered approach to data equity within TPEP.
Data Equity Framework
Through conversations with the Data Equity Advisory Board (DEAB), several key parts of a data equity framework became clear. These ideas closely match the Data Equity Framework created by We All Count, which offers a fair and organized way to plan and carry out data projects. The framework includes seven main areas that help guide data work in a clear and equitable way.
Funding
Community‑based organizations (CBOs) need stable, long‑term funding. This allows them to build the skills and capacity needed to lead community‑centered data equity projects.
Purpose
Research should focus on what communities need, not just on what researchers are curious about. Data work should support community well‑being and help improve quality of life, rather than only pointing out problems.
Project Design
Projects should be designed together with CBOs and community members. Moving away from transactional or extractive approaches creates more respectful and equitable data practices.
Data Collection & Sourcing
The data collected—and the size of the samples used—should reflect the needs, priorities, and input of the community. This ensures the information is meaningful and relevant.
Analysis
To support equity, researchers must be clear about how they analyze data and open about their own perspectives and positions. Transparency helps prevent bias and builds trust.
Interpretation
People with lived experience and CBOs with on‑the‑ground knowledge should be part of interpreting the data. Their insights help identify real gaps, challenges, strengths, and solutions.
Communication & Distribution
Communities and CBOs must have access to the data and the results. Sharing information openly is essential for equity and supports informed decision‑making.
Recommendations
The DEAB created several recommendations to help the Oregon Health Authority (OHA) bring data equity into its work with the Tobacco Prevention and Education Program (TPEP). These recommendations focus on four main areas: funding, purpose, data collection and analysis, and data sharing.
Funding
- Hire a permanent OHA staff member who understands community cultures and can support CBOs with grant writing.
- Extend grant project timelines to at least five years so CBOs have enough time to plan and complete their work.
- Make sure grants include enough funding for fair and equitable pay for CBO staff, including the time needed for data collection and analysis.
Training
- Provide OHA staff with training on diversity, equity, inclusion, data equity, and data sovereignty so they can better support CBOs.
- Offer CBOs training, tools, software, and clear processes for collecting and transferring data. This helps CBOs train their own staff and build long‑term skills.
Data Collection and Analysis
- Work with community members when planning new or ongoing data collection to make sure the purpose matches community needs.
- Use data methods that fit each situation, valuing both qualitative data (stories and experiences) and quantitative data (numbers).
- Consider allowing people to write in their race, ethnicity, or other identities instead of choosing from preset checkboxes.
Data Sharing
- Improve community access to OHA data sources, including data that is broken down into smaller groups (disaggregated data).
- Explore data‑sharing agreements or ways to connect data systems so existing state data can be combined. This would help create a clearer picture of community health and wellness.
Introduction
Project Overview and Approach
Ballot Measure 108 passed in 2020. It increased the tobacco tax and added a new tax on e‑cigarettes starting January 1, 2021. Ten percent of the new tax revenue was set aside to support communities that have been most harmed by commercial tobacco use and long‑standing health inequities. The goal of this investment was to help prevent tobacco use and support people who want to quit.
In 2021, the Oregon Health Authority (OHA) and partner organizations brought together a Community‑Based Organization (CBO) Advisory Group. This group helped design a fair process for distributing the new funding. Their recommendations shaped the CBO grant opportunity released in December 2021.
During the first funding cycle, 94 CBOs received grants to support commercial tobacco prevention and quitting efforts. The grant period ran from April 2022 to June 2023, with a total of $20 million awarded.
The Advisory Group also recommended how OHA should approach data collection and outcomes. They encouraged OHA to:
- Use different ways to measure success
- Combine multiple types of data
- Treat stories and lived experience as valid data
- Consider each community’s unique needs and priorities
- Look at nontraditional outcomes
- Include principles of data sovereignty
As community‑led work expanded under Ballot Measure 108, OHA recognized the need to better understand what data communities need and how prepared CBOs are to lead data collection. OHA decided to co‑develop a data equity framework for the Tobacco Prevention and Education Program (TPEP) and partnered with Rede Group to guide this work.
Data Equity Advisory Board (DEAB)
To build a community‑centered data equity framework, OHA invited CBOs funded by TPEP to join the Data Equity Advisory Board (DEAB). CBOs applied to participate, and OHA selected 13 organizations that represented diverse communities and regions across Oregon. One OHA representative also participated.
The DEAB met monthly from September 2023 to January 2024 and reviewed materials between meetings. Members received a stipend for their time and expertise.
The DEAB’s main responsibilities were to:
- Identify community data needs and gaps
- Recommend ways for communities to have ownership and leadership in data collection and reporting
- Identify training, resources, and technical support needed
- Help draft a data equity framework
- Review and approve final materials
Methods and Analysis
Data Collection
Data were gathered during five monthly virtual meetings held on Zoom from September 2023 to February 2024. Meetings were recorded and transcribed by a third‑party service. Notes were also collected from the Zoom chat and by a live note taker.
In October, DEAB members completed a Jam board activity to share their thoughts on public health data and data equity. Members also reviewed early summaries of meeting transcripts and provided feedback. All of these materials were used to help build the data equity framework.
Data Analysis
Analysts reviewed the meeting transcripts to identify themes, patterns, and key points. DEAB members reviewed the early analysis to make sure it was accurate and complete. Their feedback was added to the final analysis to ensure the framework reflected community perspectives.
Limitations
Self‑Selection
DEAB members volunteered to participate, which means they may have had stronger interest or more capacity than other CBOs. This could influence the results. Even so, OHA’s selection process ensured that the group represented a wide range of communities and regions.
Time Frame and Focus
The six‑month timeline made it difficult to fully explore all the issues related to tobacco prevention data. Some DEAB members also faced larger systemic challenges—such as grant and budgeting barriers—that affected their ability to participate in data projects. They shared that unless these structural issues are addressed, it will remain difficult for CBOs to fully engage in community‑based data work.
Use of Language and Definitions
Reading Levels and Accessibility
Making data and research accessible to everyone is essential. This includes using clear language, offering materials at appropriate reading levels, and considering different learning styles, sensory needs, and ways people process information.
For the DEAB, OHA must prioritize accessibility in all data work. This includes designing materials that are easy to understand and available in formats that support diverse audiences. Doing so supports equity and aligns with the DEAB’s values.
Definitions and Shared Language
At first, the goal was to create clear definitions for key terms. However, as the work continued, it became clear that addressing deeper systemic issues within OHA and the funding process was more important than perfecting definitions. Still, the following definitions helped guide the DEAB’s shared understanding.
Data Equity
Data equity means using justice, equity, and inclusion to guide every step of a data project. It focuses on shifting power, addressing inequities, and ensuring communities benefit from the data collected about them.
Data Sovereignty
Data sovereignty is the right of individuals or groups to control their own data, including how it is collected, stored, used, and interpreted.
Decolonizing Research
Decolonizing research challenges traditional Western research methods that often ignore or undervalue the knowledge of marginalized communities. It centers Indigenous and community‑based worldviews, cultural values, and languages.
The Data Equity Framework
Brief Background of Framework and Selection
How We Used and Adapted the Framework
During this project, the DEAB took part in open and thoughtful conversations about data equity. Members shared a large amount of feedback and information. They also said they needed a basic structure or starting point to help shape the final framework. They wanted guidance, but they did not want the process to be controlled by existing research or outside models. It became clear that a simple structure—a “container”—was needed to organize the ideas and information shared by the group.
To support this, Rede reviewed several data equity frameworks and selected one created by We All Count. This framework provides a clear and fair way to plan and organize data projects. It includes seven main parts:
- Funding
- Motivation (renamed Purpose for this project)
- Project Design
- Data Collection & Sourcing
- Analysis
- Interpretation
- Communication and Distribution
These seven areas offer checklists and guidance that help organizations include equity in every step of their data work. The DEAB used these seven parts as a resource while co‑creating the data equity framework for TPEP.
Importantly, Rede and the DEAB used the framework only at this basic level. They did not use the full set of tools or worksheets from the larger We All Count work plan. This choice helped ensure that the final framework was shaped by the DEAB’s own ideas, experiences, and priorities—not by any outside organization. This approach supported independence, fairness, and community leadership throughout the process.
Funding
Description
Funding plays a major role in how data projects work. It affects who has power, what resources are available, and how decisions are made. To support equity, it is important to look closely at the power dynamics in a project and make changes when needed.
Before a data project begins, researchers and CBOs need enough funding to keep their programs running, support their staff, and make improvements that benefit their communities. Equitable funding means providing the right amount of time, money, staff, and tools based on each community’s needs. This helps ensure that research is culturally specific and community‑led.
Discussion
A major theme raised by the DEAB was that data projects must be fully funded from beginning to end. Without enough funding, communities cannot act on the findings or make meaningful changes.
Many DEAB members shared that researchers often identify important community needs but do not receive enough funding to address them. As one member said:
After something is identified, like a gap or what do we have, the solutions improvements [need to be] financially backed. So yeah, we can come up with improvements, but are we getting funded to do those things in a reasonable manner[?]
The DEAB emphasized that funding and data collection are closely linked. Data is often required for competitive grants, and proposals with strong data are more likely to be funded. But CBOs vary widely in staff size, which affects how much data they can collect. This does not always reflect the true amount of work needed to serve their communities.
One member explained:
If you have a staff of three or four people and another person has a staff of 25 or 30 people, the amount of data that you can collect is not the same, and sometimes that does impact how much money you get, which then obviously impacts the services you all provide, and then data and results aren't accessible to the communities that the data were collected from.
The DEAB also discussed how funding is often distributed based on population size, which can disadvantage smaller or more isolated communities.
A member shared:
But in many cases the most vulnerable people are the smaller communities. And if they end up comparing the numbers and seeing, hey, we give a hundred thousand dollars to an organization that served a hundred people and then we gave a hundred thousand dollars to [an] organization that only served 25 people, but the 25 people, they didn't have any support in that community. They didn't have access to any resources. So the CBO had to invest a lot more funds into providing resources to those 25 in order to [equalize] the equity because they needed a little bit more to get up there.
Another member described how small CBOs struggle to compete with large nonprofits:
A lot of times, small CBOs, they're competing with a lot of these huge, big nonprofits that have everything already worked out and they have a very good workflow when it comes to applying for funding. And in many cases, it's very difficult to compete, right, because they have professional grant writers, whether they're in-house or being outsourced. And smaller organizations, they just don't have access to those resources. So having somebody from the government side that is able to also invest into that and help upcoming CBOs better prepare for opportunities when they come up, I think that would be very beneficial. So when those opportunities do come out, they have at least some kind of [an] understanding of how to apply it properly, including the fringe benefits, including maybe its line items for collecting data and submitting that.
DEAB members also raised concerns about low wages, short grant periods, and lack of access to data tools. These issues lead to staff turnover and make it difficult for CBOs to build long‑term skills in data collection.
One member said:
CBO staff are looked at as much cheaper than state staff. They get less benefits. Organizations say they'll serve a lot more families if they do that by paying their staff less so that they can have more staff. I think that's really problematic. It pushes that to happen.
Another highlighted the need for better data storage systems:
I think I want to add the fact that most of us small CBOs need to have support with buying data collection software that will allow us to store these data for as long as we want to keep them, for the simple fact that this is one thing OHA may come back for or other funders may want us to give. At the moment, most of us store our small data in small computer spaces and so on and so forth, but if we have a system that will allow us to buy something that is robust and well‑designed for the purpose of storage for a long time, that would be great.
The DEAB also recommended that OHA create a living‑wage guideline:
We are not expecting you to provide more FTE to this program than can be supported by some kind of living wage guideline. That's somewhat in line with what OHA staff are getting because we get pushback [if] we have tried to raise our wages and have equitable things and value folks being bilingual and then we get pushback like, oh, you're not giving a full FTE to this program. And it's like, well, we're paying less than the equivalent state position, that's for sure. But I don't know, just something to mention and then we need to retain folks and we need to be able to pay them fairly to do the work to be able to not burn out and serve the families and have the outcomes long term.
To succeed, CBOs need:
- Stable, long‑term funding
- Fair and equitable wages
- Enough staff time to do the work
- Tools and systems for data collection and storage
- Training and technical support
- Realistic timelines that match community capacity
- Equitable funding ensures that CBOs can collect meaningful data, act on findings, and create lasting change in their communities.
Purpose
Description
The purpose of a data project explains why the project is being done and what it hopes to achieve. In a community‑driven data project, the community leads the decision‑making from start to finish, including deciding the purpose of the data collection.
A clear purpose should explain how equity will guide the project and shape every step of the work. Because the purpose influences all decisions that follow, everyone involved must agree on it. It should be written down in a clear and complete way so that the project stays aligned with community needs.
Discussion
A major theme from the DEAB was that the purpose of a data project must come from the community—not from funders. This aligns with ideas from the decolonizing data movement, which says that communities should decide what information is collected and why. When community needs shape the purpose from the beginning, equity becomes a central part of the project.
DEAB members did not say that CBOs should own every data project. Instead, they emphasized that researchers should follow community priorities when designing data projects.
One member shared:
I'm thinking about OHA and the data that I've reported to them, I don't feel like they asked too many specific questions. They're not collecting a lot of solid data, I feel like. But something we do as an organization is we collect our own data points that fit for our community and then we compile a report that they don't even ask us to do.
Purpose and Trust
Being clear about the purpose from the community’s point of view can help build trust and increase participation. This is especially important for communities that have experienced harm from government agencies, both in the U.S. and in their countries of origin.
A DEAB member explained:
I was thinking that it would be nice to interest different stakeholders in different communities to show the benefit of data and how it will benefit each and every one when it's being collected. So people would want to participate instead of having government or different agencies, they just go out to people and say like, hey, we need this, we need that, and in many cases, especially with refugee and immigrant communities, data collection in the past was probably traumatizing. A lot of different governments…would use the data to somehow hurt the communities or use it against them.
Purpose as a Catalyst for Change
DEAB members shared that the purpose of data collection should go beyond identifying health disparities. The goal should be to support real change and improve community health. While data can help bring more funding into a community, the purpose should not be only to secure money.
As one member said:
Data should be used to improve health outcomes and come with investments [to] achieve health improvement, not just observe inequities.
Practical Considerations
Members also raised practical recommendations about how data collection should be carried out. These included:
- Avoid repeated or redundant data collection
- Time data collection so it does not burden participants
- Ensure that people are not required to provide data to receive services
- Be mindful of the tension between maintaining data integrity and avoiding harm to communities
These considerations help ensure that data projects are respectful, equitable, and aligned with community needs.
Project Design
Description
Project design is the roadmap for a research or data project. It includes the main questions the project will answer, the methods used to collect information, who will be involved, and how the results will be shared. A strong project design also includes a clear timeline, a realistic budget, and plans to address ethical issues.
Good project design helps make sure the research is done in a fair, organized, and respectful way. It also helps produce results that are accurate and useful.
A key part of equitable project design is involving community members and partners from the very beginning. When communities help shape the design, the project becomes more inclusive, ethical, and grounded in real‑life experiences. This approach supports data equity, justice, and sovereignty, and strengthens the overall quality and fairness of the research.
Discussion
The DEAB emphasized that most data and research practices are based on Western methods and worldviews. These approaches often overlook or misunderstand the experiences of many communities. To achieve data equity, communities must be involved at every stage of the research process—not just added in at the end.
For projects that include direct services, data collection should be built into the program design and the Request for Proposals (RFP). It should not be an afterthought, or a requirement added later. True data equity requires recognizing and moving away from harmful practices that have historically excluded or exploited communities.
DEAB members stressed the importance of using culturally specific practices, traditions, and languages in research. These approaches help ensure that data collection and analysis reflect the realities of diverse communities.
One member shared:
They're asking, ‘What do you need from us?’ And it's like it should be the other way. You need to learn more about... And I don't know, some people that are working in epidemiology, I mean they're probably in public health and maybe did their MPH, but maybe their training didn't include looking at DEI or they've never worked directly with the community before. And so they're skewed or not skewed. It's a different thought process for them than us who are on the ground working directly with our community. So when I see that, I think how can you as the LPHA take trainings and TA? You need it more than I need it.
Language and Mindset Matter
The DEAB also discussed how public health research has often been transactional and extractive. While large systemic changes will take time, shifting the language and mindset of researchers and program managers is an important step toward equity.
One member explained:
In general, I try to remove any words that are used to describe a bank account from conversations that center people. This is very general, but being mindful about what relationships are ‘transactional’, what communities we’ve ‘invested’ in, what initiatives have ‘paid off.’ This is a symptom of identity capitalism. We're all a bit sick with it, but if we can find other words that mean similar things, a lot of the language that feels exploitative or extractive will disappear.
Changing language helps shift attitudes and supports more respectful, community‑centered research practices.
Time Constraints and Rushed Design
Time limitations were a major concern throughout the DEAB’s discussions. Short timelines can lead to rushed planning and poorly designed programs. When there is not enough time to work directly with the community, important perspectives are left out. This can result in programs that do not meet community needs and may even reinforce inequities.
Strong project design requires meaningful consultation and collaboration. Rushed timelines make it difficult to include diverse voices and create thoughtful, effective programs.
Data Collection and Sourcing
Description
"Data collection and sourcing" means making sure the data project includes a sample that is fair, accurate, and representative. This includes reaching out to organizations and community groups of different sizes, locations, populations, and services. Doing so helps ensure that Oregon’s diverse communities are represented.
Talking with community members before collecting data provides more value than only gathering the data itself. In public health projects, the goal is to include the voices of the communities involved and to design data collection methods that are efficient and focused on equity.
This approach supports the development of data collection priorities and practices that reduce social inequities, especially in communities that have been historically underrepresented.
Discussion
Some community‑based organizations (CBOs) were interested in leading their own data projects. However, many were concerned about having enough time, staff, and skills to do this work. Increasing the capacity of CBOs to run their own data projects could help broaden the perspectives used in research and strengthen equity in data analysis. At the same time, some DEAB members felt that CBOs should not be expected to become data experts, especially because current funding does not provide enough time or resources to build these skills while also managing their regular work.
...sometimes it feels like it would be great if there were the folks with data expertise and the resources that the state and others have, that the goals could come from the community, but that the community didn't have to then do the data work because that's a specific expertise and then you can collect data that's not even meaningful if you miss some of the technical parts. So how can we get those things to actually work together effectively instead of the goals being driven by the data people and both sides being separate…
DEAB members also raised concerns about how often data is aggregated. Aggregation is often used when sample sizes are small or when there are concerns about whether the data can be generalized. However, members emphasized that they need disaggregated data to understand what is happening within their specific communities. This requires shifting ideas about what counts as “valid” data. Large datasets often cannot identify the specific services or interventions needed for smaller or underrepresented groups. In many cases, stories, lived experiences, and other qualitative data provide a clearer picture of what people are experiencing and what supports they need.
...when you go into the community and when you talk to people and sometimes you get a direct feel for the changes undergoing in the community. I'll give you an example for the Chinese American community, and I know it's very cultural specific for the Chinese American. They eat together for any social occasions, they normally gather in the restaurant. And before, it's a custom for people to show respect to each other and they offered cigarettes to each other and nowadays, it happens less and less and whatever reason. So, it's a lot of reason underneath, but that's a trend. And those trends might not be reflected in the public health data but we know it's going, it's happening.
Another issue raised was the need to stop comparing community data to the dominant (White) population. Instead, communities should be asked what standards or metrics matter to them. The focus should be on the experiences and needs of the community whose data is being collected, rather than on comparisons between groups.
One of the things that I shared with the group was that the way data is presented oftentimes is very biased in that we're oftentimes putting the dominant society as the standard. So we're comparing other communities to this dominant white society or population, rather, and saying like, how are they doing in comparison to this group?
Analysis
Description
Data analysis is a critical part of any data project. It can either support equity or reinforce existing inequities. Analysts bring their own experiences and perspectives to the work. If these perspectives are not examined, they can introduce bias into the analysis.
According to We All Count, “variable selection, data processing, category collapsing, proxies, how variables are used, and the relationships and mechanisms between each variable reflect the evidence, experience, and understanding of the people creating the model.” This highlights the need for a clear and intentional approach to analysis, including how equity is built into the process. It also requires transparency about the analysts’ perspectives and positionality, and how these factors may influence the results.
Discussion
Some community‑based organizations (CBOs) were interested in leading full data projects for their communities. However, many were concerned about having enough time, staff, and skills to do this work. Increasing CBOs’ capacity to conduct their own data projects could help broaden the perspectives used in research and strengthen equity in data analysis. At the same time, some DEAB members felt that CBOs should not be expected to become data experts, especially because current funding does not provide enough resources to build these skills while also managing their regular responsibilities.
...sometimes it feels like it would be great if there were the folks with data expertise and the resources that the state and others have, that the goals could come from the community, but that the community didn't have to then do the data work because that's a specific expertise and then you can collect data that's not even meaningful if you miss some of the technical parts. So how can we get those things to actually work together effectively instead of the goals being driven by the data people and both sides being separate…
As noted in the Data Collection & Sourcing section, DEAB members repeatedly raised concerns about how often data is aggregated. Aggregation is often used when sample sizes are small or when there are concerns about generalizability. However, members emphasized that they need disaggregated data to understand what is happening within their communities. This requires shifting ideas about what counts as “valid” data. Large datasets often cannot identify the specific services or interventions needed for smaller or underrepresented groups. In many cases, stories, lived experiences, and other qualitative data provide a clearer picture of what people are experiencing and what supports they need.
...when you go into the community and when you talk to people and sometimes you get a direct feel for the changes undergoing in the community. I'll give you an example for the Chinese American community, and I know it's very cultural specific for the Chinese American. They eat together for any social occasions, they normally gather in the restaurant. And before, it's a custom for people to show respect to each other and they offered cigarettes to each other and nowadays, it happens less and less and whatever reason. So, it's a lot of reason underneath, but that's a trend. And those trends might not be reflected in the public health data but we know it's going, it's happening.
Another concern was the common practice of comparing community data to the dominant (White) population. DEAB members noted that this approach reinforces bias. Instead, communities should be asked what metrics matter to them. The focus should be on the experiences and needs of the community whose data is being collected, rather than on comparisons between groups.
One of the things that I shared with the group was that the way data is presented oftentimes is very biased in that we're oftentimes putting the dominant society as the standard. So we're comparing other communities to this dominant white society or population, rather, and saying like, how are they doing in comparison to this group?
Interpretation
Description
Interpreting data means taking time to understand what the information really shows. It is more than just looking at numbers or charts. Researchers study patterns, trends, and connections to learn what the data might mean for the community.
Good interpretation brings together different types of information, including numbers (quantitative data) and people’s experiences or stories (qualitative data). Researchers also think about outside factors that might affect the results. They recognize that their own perspectives can influence how they understand the data.
During this process, researchers compare the results to their original questions or ideas. They talk about what the findings might mean in real life and how they connect to what is already known. Clear and honest interpretation helps turn raw data into useful knowledge.
When researchers explain their findings in a transparent and thoughtful way, it helps communities, policymakers, and other researchers make informed decisions. Strong interpretation also supports future research and helps build a deeper understanding of public health issues.
Discussion
The DEAB believes that community‑based organizations (CBOs) and people with lived experience should be active partners in reviewing and interpreting data. When communities help make sense of the information collected about them, the process becomes more fair, collaborative, and accessible.
One community member explained the importance of involving people in decisions about how their data is shown and compared:
And so oftentimes, you're not coming to [the] community and saying, ‘what is the ideal rate? What do you want the numbers to be?’ Not like ‘this is what we're comparing you [with] as the default’; and so giving [the] community an opportunity to weigh in on how their data is presented and how their data is compared or not compared to other populations is imperative.
Another community member shared how cultural practices can be misunderstood when researchers do not take time to learn from the community:
And to tell you how much some of these cultural behaviors don't really mean harm to communities. The [inaudible] chew tobacco a whole lot. They're coming for [a] tobacco cessation program with us. But they'll bring [it] for me [as] presents, that present is the tobacco that I've tried to help them to stop chewing. So, I do ask them, ‘Do you know what is bringing you here? Yeah, you're doing [a] program to stop chewing tobacco. You're bringing it for me to chew.’ For them, that's the only way they can show me love.
These examples show why interpretation must include community voices. Without this, researchers may misunderstand behaviors, overlook cultural meaning, or draw conclusions that do not reflect real community experiences.
Another challenge is the fast pace of public health research. When data projects are limited to one or two years, the results often miss important trends and long‑term patterns. Trying to understand a community with only a short window of data is like trying to shrink a whole dictionary into a few pages—it leaves out too much.
Taking more time allows for deeper analysis and a clearer picture of what communities are experiencing. This leads to better public health strategies and more accurate results.
A community member described how short timelines can harm programs and funding:
I think some of the best data and analytics of program participation, outcomes connected to different program engagement is really multi-year stuff, but we're all kind of always looking at 12 months, if you're lucky, 24…for example... It [current project] was supposed to be 12 months of funding, but we didn't get the contract until almost January. And so then we really only had six months of work. And then we were told that it was being cut because we ‘didn't hit very high numbers’. So clearly we ‘didn't need to be doing that work’. And this is enrollment for Healthier Oregon, and now is the time when we're finally expanding eligibility. So the fact that we're using six months of data to cut next year's funding for a population that is finally expanding to where we want to be enrolling more people, it's like we're looking at such a short timeframe and then we're really not understanding the trends in the community.
This story shows how rushed timelines can lead to decisions that harm communities, especially when funding is tied to short‑term data.
Overall, meaningful interpretation requires time, transparency, and true partnership with the community. When community members help guide how data is understood and used, the results are more accurate, respectful, and useful for everyone.
Communication and Distribution
Description
Sharing research in clear and accessible ways is an important part of public health work. Researchers use many methods to share what they learn. Some of these methods include academic journals, conferences, and student projects like theses and dissertations.
But sharing information should not stay inside academic spaces. It also needs to reach the broader community. This can happen through news stories, websites, social media, and educational events. These tools help make information easier for everyone to understand and use.
Working together is also key. Community groups, policymakers, and researchers can create simple summaries, policy briefs, and online posts that help people learn and take action. Using many different communication methods helps reach a wide range of people, including students, community members, and decision‑makers.
Strong communication and distribution strategies help make sure public health research reaches the people who need it. When information is shared clearly and widely, it supports better decisions and healthier communities.
Discussion
Open access to data is important because it helps build trust, transparency, and collaboration between researchers and the community. When people can see and use the data that comes from their own communities, it supports shared learning and stronger relationships.
When data is kept from the community, it creates a transparency gap. This makes people question how information is being used and why it is not being shared. Many communities already have low trust in research because of past harm, discrimination, and exploitation. Too often, data has been taken from communities, used for someone else’s benefit, and never returned in a useful way.
To rebuild trust, communities need to be included throughout the entire research process. This means involving community members from the beginning, sharing updates often, and making sure information is easy to understand and access.
In public health, many people want clearer communication and wider sharing of data. At the same time, communities are worried about how their information is passed to government agencies or private contractors. These concerns show why transparency is essential. People deserve to know how their data is used, where it goes, and how it affects their community.
Clear communication helps build trust and encourages community members to take part in data projects. When people understand how data is used and how it impacts them, they can make informed choices about participating in research.
Some community members have also raised questions about data equity. They ask what it would look like if communities had the same power that researchers and institutions have had—owning their data, controlling how it is used, and benefiting from it without barriers. These questions help guide conversations about data equity and decolonizing data practices.
There are also concerns about what happens when research results affect funding. Sometimes accurate, unbiased data can lead to outcomes that harm a community, such as losing resources. This raises important questions:
- What matters more—data accuracy or community well‑being?
- How can we protect communities from harm while still sharing honest results?
- How do we make sure data supports communities instead of putting them at risk?
Another challenge is the sense of urgency in public health research. When research is rushed, communities often do not have time to participate, share their experiences, or help shape the work. This can lead to missing information, misunderstandings, and results that do not reflect the community’s needs. Rushed timelines also make it harder to create clear materials that explain findings in community‑friendly ways.
To address these issues, researchers need enough time to involve the community, share information clearly, and listen to feedback. Taking time helps ensure that programs, policies, and interventions are respectful, culturally relevant, and effective.
Public health research works best when data is shared in ways that are easy to understand, available in the community’s preferred languages, and accessible to everyone. When communication is clear and ongoing, communities can participate fully and benefit from the data they help create.
Conclusion and Recommendations
Many communities have been practicing data equity and community‑led work for a long time. But these efforts are often ignored by government systems and other institutions shaped by white supremacy. Real progress starts with recognizing the knowledge and experience that already exist in our communities. Top‑down decisions often miss what people actually need.
We need a new approach that centers community voices. This means listening to community members, respecting their expertise, and using their ideas to guide decisions. Right now, the funding system makes this hard. Many community‑based organizations (CBOs) don’t have stable funding, can’t pay fair wages, and struggle to keep staff.
The idea is simple: give CBOs the funding they need and follow their guidance. The people closest to the issues understand them best. Their leadership should shape how programs are designed and carried out.
To build strong and lasting community‑driven data work, funding systems must change. CBOs need enough money, flexibility, and support to grow their skills and capacity. Government agencies, including the Oregon Health Authority (OHA), also need to provide ongoing support.
In short, meaningful change happens when CBOs are well‑funded, respected, and trusted to lead the way.
Data Equity Recommendations
Funding
- Hire a permanent, culturally-informed OHA staff person to support the grant writing process for CBOs
- Lengthen grant project lifecycles to a minimum of 5 years
- Ensure grant opportunities include enough resources to provide equitable pay to CBO staff to perform their job
- responsibilities, including data collection and analysis
Training
- Provide relevant training (including diversity, equity and inclusion, data equity, and data sovereignty) to OHA staff who support CBOs
- Provide data collection training, data collection tools, data system software, and data transfer processes to CBOs, so
- they can support and train their own staff to conduct research
Data Collection and Analysis
- For new or recurring OHA data collection efforts, work with community representatives to make sure the purpose of
- the data collection matches community needs
- Apply relevant data measurements that are appropriate to each situation; valuing the qualities of both qualitative data
- (stories) and quantitative data (numbers)
- Explore options where survey respondents fill in their race/ethnicity, etc. instead of using the checkboxes
Data Sharing
- Increase access to existing OHA data sources, including disaggregated data
- Explore opportunities for data sharing agreements or data system integration to be able to compile existing state data
- to gain a more comprehensive understanding of community health and wellness