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Maryland Social Determinants Data

The Social Determinants of Health (SDOH) are non-medical factors that influence health outcomes, and include the conditions in which people are born, grow, work, live, and age, as well as the wider set of forces and systems shaping the conditions of daily life (World Health Organization, 2025).

 

Similarly, the Social Determinants of Learning (SDOL)™ framework defines SDOLs as "social and structural factors outside the individual learner, often beyond the traditional reach of teachers and schools, that can affect learning” (Levinson & Cohen, 2023). These include healthcare access and quality, economic stability, neighborhood and built environments, social inclusion and non-discrimination, educational access and quality, and family group and structural conflicts.

In both frameworks, social determinants can be predictive or correlated to both health and educational outcomes—people without access to a vehicle may be unable to access high-quality healthcare and educational services; people living with disabilities may face structural and discrimination-related barriers to accessing equitable healthcare and education services compared to able-bodied peers.

The infographics in this section highlight over 20 different social determinants, including measures of poverty, energy burden (the percentage of one's annual income spent on energy costs), housing burden (spending 50% or more of one's annual income on housing), and the prevalence of disabilities.

Please find below some tips to help users access and utilize the information they'll find:

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  • Numbers that are expressed as percentages represent the percentage of the population experiencing a given social determinant.
     

  • The counties highlighted generally represent the lowest and highest values of a given measure.
     

  • The Pearson Correlation Coefficient shows the correlation between low literacy proficiency and a given health condition. Numbers range from -1.0 (a Perfect Negative correlation) to 1.0 (a Perfect Positive correlation). The closer the number is to -1.0 or 1.0, the higher the correlation between low literacy skills and the social determinant.
     

  • IMPORTANT NOTE:Because Maryland has only three counties that lie within the Appalachian Region, both regionwide averages and Pearson Correlation Coefficient findings should be used with caution, as they may not be generalizable.
     

  • Our sources for these data may be found on our Resources page in the Citations section.

% Bridges Fair - Poor
(2024)

Regional Prevalence
Statewide Prevalence
National Prevalence

71.6%

67.7%

55.8%

Pearson Correlation Coefficient

-0.5

The image presents a horizontal correlation scale ranging from -1.0 to 1.0. On the left end, -1.0 is labeled "Perfect Negative Correlation" and on the right end, 1.0 is labeled "Perfect Positive Correlation." The center, marked as 0, is labeled "No Correlation." The scale is segmented into different correlation strengths: "Strong Negative Correlation" (-1.0 to -0.7), "Moderate Negative Correlation" (-0.6 to -0.4), "Weak Negative Correlation" (-0.3 to -0.1), "Weak Positive Correlation" (0.1 to 0.3), "Moderate Positive Correlation" (0.4 to 0.6), and "Strong Positive Correlation" (0.7 to 1.0).
The image shows a graphic about bridge conditions in Maryland, focusing on Appalachian counties. On the left, text states, "Bridge Conditions in Maryland" and "Nearly 3 out of every 4 bridges (71.6%) in Maryland’s Appalachian counties are classified as being in either 'Fair' or 'Poor' condition." A map of Maryland highlights three Appalachian counties: Garrett, Allegany, and Washington, each shaded in shades of purple corresponding to the percentage of bridges in "Fair" or "Poor" condition, with Garrett at 79.9%, Allegany at 63.3%, and Washington at 71.7%. A color gradient key indicates percentages from 63.3% to 79.9%. To the lower right is an image of a stone bridge with surrounding greenery, alongside the logo "APPLI, Appalachian Learning Initiative, www.appli.org."
Full-Width Graphic
The image shows a graphic about bridge conditions in Maryland, focusing on Appalachian counties. On the left, text states, "Bridge Conditions in Maryland" and "Nearly 3 out of every 4 bridges (71.6%) in Maryland’s Appalachian counties are classified as being in either 'Fair' or 'Poor' condition." A map of Maryland highlights three Appalachian counties: Garrett, Allegany, and Washington, each shaded in shades of purple corresponding to the percentage of bridges in "Fair" or "Poor" condition, with Garrett at 79.9%, Allegany at 63.3%, and Washington at 71.7%. A color gradient key indicates percentages from 63.3% to 79.9%. To the lower right is an image of a stone bridge with surrounding greenery, alongside the logo "APPLI, Appalachian Learning Initiative, www.appli.org."
Social Media Graphic
The image shows a graphic about bridge conditions in Maryland, focusing on Appalachian counties. On the left, text states, "Bridge Conditions in Maryland" and "Nearly 3 out of every 4 bridges (71.6%) in Maryland’s Appalachian counties are classified as being in either 'Fair' or 'Poor' condition." A map of Maryland highlights three Appalachian counties: Garrett, Allegany, and Washington, each shaded in shades of purple corresponding to the percentage of bridges in "Fair" or "Poor" condition, with Garrett at 79.9%, Allegany at 63.3%, and Washington at 71.7%. A color gradient key indicates percentages from 63.3% to 79.9%. To the lower right is an image of a stone bridge with surrounding greenery, alongside the logo "APPLI, Appalachian Learning Initiative, www.appli.org."
Allegany County, MD 11.jpg
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