Social Determinants of Health: Vulnerable Populations

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  • Poverty
    • In this module, we will discuss how poverty relates to health as we examine the interrelationships between poverty and other social determinants of health. In lesson one, we will define poverty, as we explore how poverty both causes and is caused by poor health. We will also look at some policy perspectives aimed at eradicating poverty. In lesson two, we will continue our discussion from lesson one, as we evaluate different approaches, perspectives and solutions to ending poverty. We will also consider how success is measured when evaluating the effectiveness of these proposed interventions.
  • Women
    • In this module, we will consider the gender-specific impact of social determinants of health on women. In lesson one, we will review what defines a health inequity for women and examine how gender acts as an axis of health disparity. Lesson two focuses on reproductive health and the need for contraception. We will look at how the SDOH shape resources and accessibility for women at global and national levels, and how reproductive rights are tied to human rights for girls and women. Lesson three continues to build on the content from previous lessons, with a more in-depth look at maternal health. We will investigate how the SDOH impacts aspects of maternal health with a focus on pregnancy related mortality and its prevalence on global and national levels. We will also examine how structural racism drives health disparities and specifically, maternal outcomes. In lesson four, we will summarize the factors that shape gender-based health disparities and the impact on women, while investigating policy-based strategies and resources to improve health outcomes for women while addressing SDOHs.
  • Gender and LGBTQI+ Health
    • In this module, we focus on the social determinants of health in LGBTQI+ populations. In lesson one, we will define important terminology related to LGBTQI+ health needs as we look at LGBTQI+ vulnerabilities to the social determinants of health. In lesson two, we will examine how heteronormativity and cisnormativity can act as negative social determinants of LGBTQI+ health and wellbeing as we consider how media and language can perpetuate these inequalities. In lesson three, we consider how microaggressions, discrimination, and implicit bias can contribute to a healthcare system bias that negatively impacts the accessibility and quality of care received by LGBTQI+ patients. In lesson four, we will review key dates in LGBTQI+ history in the United States, as we investigate social and structural level changes that can improve LGBTQI+ health outcomes.
  • Family
    • In this module, we will examine the social determinants of health for families with young children and discuss policy-based strategies for improving health outcomes. In lesson one, we’ll explore the Rainbow Model as a way to understand how social determinants affect the health of young children. We will also review the different pathways through which socioeconomic circumstances influence health and contribute to child health inequalities. Applying what we learned in the previous lesson, in lesson two, we will look at a case study in order to investigate how social determinants influence a family’s ability to promote child health. Finally in lesson three, we will analyze some strategies for improving health outcomes for families with young children through policies that address social determinants of health.
  • Data Applications: t-test Analysis and Box Plot Visualization
    • This module will focus on analyzing, displaying and interpreting social determinants of health data, with a particular focus on comparing social determinants by group. Lesson one will provide an overview of t-test analysis and box plot visualization. In lesson two, we will learn how to conduct t-test analyses and create boxplots in R. Using the NHANES dataset, we will compare general health and Hgb a1c by gender. Using the Omaha System dataset, we will compare total signs & symptoms, social determinant of health signs & symptoms, and income signs & symptoms by gender. Finally, we will discuss how to interpret the results of our analysis as we visualize our findings using boxplots.