Using Data to Identify Priorities and Health Inequities
Using data to understand the scope of health inequities can help identify which members of the community are
affected by inequities. Rural communities can use data to compare selected measures between populations or among
geographic areas in the community and identify where differences are greatest. When using data to identify
priorities, rural communities should understand the importance of
Historical and Political Context and
Engaging Community Members
Affected by Health Inequities.
Rural communities can assess a range of potential quantitative data to understand key differences among
populations and areas of greatest needs:
Rural communities can also collect information directly from the community and those who work with people who
experience inequities. Potential qualitative data sources include:
Community members who experience inequities
Lay health workers and social services providers with deep ties to communities that experience inequities
Community groups and organizations that serve people who experience inequities
A range of agencies and departments that address the social determinants of health affecting communities
that experience inequities
Rural communities may consider how other rural local health departments and collaboratives have used data to
identify the fundamental causes of health inequities. For example, the Minnesota Department of Health requires
grantees of its Statewide Health Improvement Partnership to complete a Health Equity Data Analysis
(HEDA). The HEDA process includes the following steps:
Connection and later (Re)connection,
which involves expanding understanding of the social determinants of health and then exploring how health
outcomes are linked to those social conditions. In their HEDA, rural Chisago County
examined the connection between employment, hours worked, education, and prevalence of type 2 diabetes in
identifying populations in the community that experience health inequities. Rural Rice
County Public Health conducted a HEDA based on their WIC clinic data that showed Latino children
were disproportionately experiencing pediatric obesity. The Health
Equity Plan also includes a commitment to “regularly collect, analyze, and report data related
health equity and/or social determinants of health.”
Differences, or finding
differences in outcomes or behaviors among groups in the community. Southwest Health and Human Services
conducted a HEDA to explore the state and causes of rural poverty in rural Lincoln County. The HEDA
identified differences in smoking rates, obese weight status, hypertension, and high cholesterol between
Lincoln County residents and those of the tri-county region.
Causes and Conditions,
or identifying systemic roots for differences in health outcomes. While every step of the HEDA process
offers opportunities for members of the communities to help identify the contributors of health outcomes,
qualitative data collection is central to identifying causes and conditions. As part of their HEDA,
rural Meeker McLeod Sibley (MMS) Healthy Communities conducted interviews with participants of the Women,
Infants, and Children (WIC) program and providers who work with WIC. Key causes and conditions included
chronic stress, lack of quality housing, lack of employment opportunities, and lack of inclusion in
Examples of Rural Communities Using Data to Identify Priorities
The Health Equity and Access in Rural Regions (HEARR) project
is using community and population mapping to identify opportunities for community-led demonstration
The Southeast Arizona Area Health Education
Center (SEAHEC) Healthy Farms Program began as a farmworker health initiative in the Winchester
Heights community or “colonia” of rural Cochise County, AZ. The impetus for the initiative was a
community health needs assessment that revealed deep needs of community members. The assessment shows that
most residents of Winchester Heights were Latino farmworkers who faced numerous structural barriers in
accessing healthcare and improving the conditions in which they worked and lived.
A core aspect of the American Indian Cancer Foundation's
(AICAF) work involves assisting tribes with accessing reliable cancer data. AICAF works with federal and
state agencies to raise awareness about the misclassification and misrepresentation of data on American
Indian/Alaska Native communities.
The Hogg Foundation's Collaborative Approaches to Well-Being in Rural
Communities (WRC) initiative is promoting place-based and community-driven solutions to mental
health in rural Texas. During the planning phase of WRC, community collaboratives were funded to complete
baseline assessments and build capacity in evaluation and systems change.
The County Health Rankings & Roadmaps describes data
disaggregation as a key consideration for illuminating health inequities. Data disaggregation can be
particularly important in rural communities with small population sizes, where aggregation of data may mask
differences in health outcomes among different groups of people. Where data disaggregation is not possible,
rural communities can consider conducting additional data collection through traditional methods like surveys
and interviews and less traditional approaches like participatory community mapping and Photovoice.
Examples of Clearinghouse Programs
Resources to Learn More
Health Equity: Data and
Provides links to datasets related to health equity.
Organization(s): University of California Berkeley Library
Health Equity Through Data Collection AND Use: A Guide for Hospital Leaders
Describes best practices used by hospitals for collecting and utilizing patient race, ethnicity, and language
data to identify health inequities, increase access to care, and improve health outcomes.
Organization(s): American Hospital Association (AHA), Health Research and Educational Trust
Provides links to mapping resources and data to illustrate health disparities at the local level and improve
community development and health outcomes across the U.S. Offers the Rural Data Portal providing data on the
social, economic, and housing features of rural communities.
Organization(s): Build Healthy Places Network
Data to Reduce Disparities and Improve Quality
Presents healthcare organizations and stakeholders recommendations on using data to identify, prioritize, and
reduce healthcare disparities. Discusses the value of multi-stakeholder collaboratives working together to
increase the potential to reduce health inequities.
Author(s): DeMeester, R., Mahadevan, R., Cook, S, et al.
Organization(s): Advancing Health Equity
Using Data to
Reduce Health Disparities and Improve Health Equity
Offers guidance and examples for hospitals and health systems on applying data to address the systemic causes of
Organization(s): American Hospital Association (AHA) Center for Health Innovation