Education and health outcomes


Education and health outcomes

Exploring numeracy, literacy, and health outcomes in adulthood

Education is one of the drivers of health typically established early in life and often measured through assessments for literacy and numeracy. Our three-part series offers actionable takeaways and examines health equity data to better understand the relationship between literacy and numeracy and chronic condition management, health behaviors, and health care access.

The ABC’s of education and health outcomes

Knowledge of the health care system and wellness is often cited as a precursor to good health outcomes. Academic literature shows that higher literacy is associated with greater knowledge of health services.1 There are also correlations to certain health behaviors, conditions, and outcomes.1,2 It’s noteworthy, though, that many of these studies are observational and cross-sectional in design, often representing small to moderately sized cohorts, and frequently measuring a variety of outcomes, so pooling the results to solidify the size and direction of findings is more difficult.1,2,3,4,5

The education-specific root cause of the differential health outcomes is challenging to quantify definitively as there are other variables that modify and mediate the relationship, regularly tempering strong initial correlations in studies down closer to the null.1,5,6,7 In recent decades, there have been major shifts that potentially have an impact on the relationship between literacy and health outcomes.8 The first is the increase in patients’ involvement in their own health journeys and medical decision-making. The second is the explosion of medical information readily available to the public. While both are great advancements, they also underscore the importance of better understanding the relationship between literacy and health as underlying differences may exacerbate inequities of access and outcomes in this new environment.

Additionally, resources—both in health care but especially in education—are limited, and population-level insights like this can give key decision-makers and leaders the data to act in specific contexts and communities. When communities are identified, there may be an opportunity to dive deeper with rigorous qualitative and quantitative assessments alongside the communities to increase our understanding of the causal mechanisms using methods that enhance the current literature. Here we’ll begin to explore and ideate around some potential intervention pathways and settings that could be tested with variable impact measurement timelines (i.e., interventions on school-aged children require a longer follow-up period than interventions on adults).

Part one: Numeracy, literacy, and chronic condition management

Counties with lower numeracy and literacy tend to experience higher rates of high cholesterol, diabetes, obesity, and high blood pressure. These conditions require general at-home management, as well as a strong understanding of numeric values such as calorie count and blood cholesterol, blood sugar, and blood pressure levels for successful management of these conditions.

Other factors such as income may reverse the relationship we observed between literacy, numeracy, and these chronic conditions. However, we found that while the prevalence of the conditions is overall higher in progressively lower-income counties, within the same income group, the relationship between literacy, numeracy, and these conditions remains the same.


I County level data health outcomes data is sourced from CDC PLACES, county level competency data is sourced from National Center for Education Statistics (NCES), and county income data is sourced from County Health Rankings which was derived from the American Community Survey (ACS). See the methodology section for additional details.

II A county is defined as having low proficiency if the average Program for the International Assessment of Adult Competencies (PIAAC) score is below 226 (out of 500), medium proficiency between 226-275, and high proficiency above 275. These cutoffs correspond to the 3 levels used on the PIAAC Skills Map.

III Upper Third of National Income is defined here as the top third of US counties with respect to average income. Lower third of National Income is the bottom third of US counties with respect to average income. The moderate group is the middle third.

IV No counties had both high numeracy and were in either the lower or middle third of income. Additionally, no countries had both low literacy and were in the upper third of income.

Taking action

Through this population-level analysis, particularly in communities in the lower-income range, there’s a negative relationship between literacy, numeracy, and prevalence of these chronic conditions. Using these findings to narrow down into the communities that scored lower and had higher prevalence of these chronic conditions, one could begin to formulate potential interventions. Of course, there may not be a one-size-fits-all solution, and there would need to be significant involvement from the community to understand what interventions are desired and may work. How could the health care and education sectors potentially collaborate?

Support can come in many forms:

  • Developing partnerships to offer basic health care services through local health providers directly at school to remove barriers for students and parents.
  • Co-developing age-appropriate additional lessons during school, created and/or co-taught by local health care providers, on health care access, chronic conditions, at-home disease management, healthy habits, and evidence-based practices.
  • Collaborating to organize culturally humble, relevant health and health care workshops that are aligned with the values of the community and led by local health providers.
  • Creating partnerships to bring farmers markets to schools and co-organize healthy food preparation workshops during evenings or weekends for the students and families

Lastly, it can be important to measure the effect of these interventions not only to monitor efficacy and quality, but also to be able to illuminate the education-related pathways to health outcomes and share the interventions that work well and can be implemented elsewhere.

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HExA is developed and produced by the Deloitte Health Equity Institute Data and Analytics (D&A) team, Pavan Kumar Bhoslay and Nivedha Subburaman, led by D&A Manager Elya Papoyan and with the leadership support of DHEI Senior Manager Nicole Kelm and Managing Director Jay Bhatt.

DHEI Health Equity Research Manager Maningbe Keita Fakeye provides invaluable subject-matter expertise on methodology and interpretation.

Manager of Computational Social Science of Data Science and Survey Advisory (DSAS) David Levin contributes to development of the data strategy.

The project also benefits from contributions from others, including Grace Ann Aldridge and Meghna Patel.


Data and Analytics is a core function of the Deloitte Health Equity Institute (DHEI) that helps enable the mission of the Institute through novel and tailored health equity analytics that can catalyze action. Having this data-driven functionality as a foundation of our work allows us to explore data as the primary driver of our efforts rather than a secondary support to existing programs.

To read the full methodology behind the HExA education data, click below.

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