“Stories are just data with a soul.
- Dr. Brene Brown, University of Houston”
The world we live in today is run by data, from smartphones to smartwatches to Google maps. Data plays a key role in understanding the health status of individuals and populations. A growing body of data highlights the link between health inequities and social needs (e.g. housing, food, income) and social determinants of health, conditions where people live, work, learn, work and play affect health risks and outcomes.[1] “Without a clear understanding of existing health inequities, well-intentioned strategies may have no effect on or could even widen health inequities.”[2]
Strategies to gain a clear understanding of health inequities and take action include:
Check assumptions about why health inequities exist.3 Do not assume why certain health issues do or do not impact populations. Structural inequities, cultural norms, and implicit bias play a role in achieving health equity. For example, LGBTQ youth experience high rates obesity, however, assumptions or implicit bias may impede taking action to understand why this population is disproportionately impacted. Test hidden biases by taking the Harvard Implicit Association Test, developed by Psychologists at Harvard, the University of Virginia and the University of Washington.
Collect Comprehensive Quantitative and Qualitative Data to Gain a Clear Picture. Collecting quantitative and qualitative data is important to gain a comprehensive understanding of the social, economic, and physical factors that impact an individual’s ability to healthy where they live, work, learn, and play. Quantitative data helps define the “what” and qualitative data helps tell the “why”. Using both methods helps to shape a comprehensive understanding of health inequities.
Community Engagement.2,3 This is arguably the key strategy to understand and address health inequities. Hearing the voice of the community impacted directly by health inequities is crucial and sheds light on the data based on their experiences. Community members should be engaged throughout the data collection process, including planning, data collection, implementation and evaluation, to support collaboration and sustainability of any interventions that may result from the data. Data collection methods to gather input from the community may include focus groups, key informant interviews, surveys, photovoice, community forums, etc. Whatever the method, the community voice is crucial.
Frame data by demographic context.[3] Framing data collected by social, economic and environmental conditions, as well as geographic location can help to better understand the root causes of health outcomes. Neighborhood disparities may exist that are hidden by state, county, and city level data. For example, high obesity rates in a rural underserved community may be attributable to low access to healthy foods, the quality of food served in food distribution programs, or poverty. Without breaking down the data, it only shows high obesity rates.
Collect Data on Social Determinants of Health.3 Identifying and collecting data on indicators that reflect systemic or social determinants of health is equally important to help shape the narrative. Health equity metrics recommended to understand the social determinants of health focus on structural drivers, such as rates of incarceration by race/ethnicity, community determinants, such as access mobility and transportation, the food retail environment, or workplace safety; and healthcare services, such as culturally or linguistically appropriate care. Learn more from the Centers for Disease Control and Prevention[4] and the Prevention Institute[5] about SDOH tools and indicators.
Use Nontraditional Data Tools.3 National databases, health systems, universities, and health departments are traditional key sources of local data important to help shape understanding. Seek out data from non-traditional partners, such as city planners or police departments, which may also reveal inequities. GIS Mapping, social media, wearable technology, or mobile phone data are just some nontraditional data types that can help understand health inequities.[6] Consider the information being sought and how different data tools can help tell the story.
Data Sharing. The ability to share and analyze data can be a key strategy to understand and address health inequities. Data sharing is an evidence-based practice that is recognized for the value it can bring to shaping health outcomes and improving community health. Learn more from The Network for Public Health Law[7] on health information and data sharing to take action.
Take the opportunity learn to reflect if data is being collected to help tell the story about health inequities. Collecting the right kind of data and using it to take action on health equities is a necessary strategy to achieve health equity.
References
[1] National Center for Chronic Disease Prevention and Health Promotion. (2019). Health Equity. https://www.cdc.gov/chronicdisease/healthequity/index.htm
[2] Centers for Disease Control and Prevention – Division of Community Health. A Practitioner’s Guide for Advancing Health Equity: Community Strategies for Preventing Chronic Disease. Atlanta, GA: US Department of Health and Human Services; 2013. https://www.cdc.gov/nccdphp/dch/pdf/HealthEquityGuide.pdf
[3] Office of Health Equity, Colorado Department of Public Health & Environment. (n.d.). Framing Data to Advance Health Equity. https://www.cdc.gov/nccdphp/dch/pdf/HealthEquityGuide.pdf
[4] Centers for Disease Control and Prevention, Social Determinants of Health. (2018). Tools for Putting Social Determinants of Health into Action. https://www.cdc.gov/socialdeterminants/tools/index.htm
[5] Prevention Institute. (2015). Measuring What Works to Achieve Health Equity: Metrics for the Determinants of Health. https://www.preventioninstitute.org/sites/default/files/publications/Measuring%20What%20Works%20to%20Achieve%20Health%20Equity%20_Full_Report.pdf
[6] Seeskin, Z., LeClere, F., Ahn, J., & Williams, J. (2018). Uses of Alternative Data Sources for Public Health Statistics and Policymaking: Challenges and Opportunities. https://www.norc.org/PDFs/Publications/SeeskinZ_Uses%20of%20Alternative%20Data%20Sources_2018.pdf
[7] The Network for Public Health Law. (2020). Health Information and Data Sharing. https://www.networkforphl.org/resources/topics/health-information-and-data-sharing/#learn-more